Federated Learning Market

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Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region - Global Forecast to 2028

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Report Code: TC 7866 Apr, 2022, by marketsandmarkets.com

According to the AS-IS scenario, the global Federated Learning Market size is estimated to grow from USD 127 million in 2023 to USD 210 million by 2028, at a CAGR of 10.6% during the forecast period. The major growth factor of the federated learning market is that it allows numerous players to develop shared, strong & deep training models while sharing important information, permitting crucial concerns such as data protection, confidentiality, information-privileged access, and accessibility to large datasets to be addressed. Federated learning enables several institutions to perform collaborative research without sharing sensitive patient data in the healthcare sector. This accelerates the drug discovery process by allowing models to be trained on diverse data sets, and it leads to precise predictions. Federated learning in IIoT advances the development of predictive models for maintenance & optimization from the data aggregated from various sources without sharing raw data between them. It ensures data security and improves operational efficiency in diverse industrial scenarios.

Federated Learning Market

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Market Dynamics

Driver:  Rising demand for personalized AI models across verticals

Federated learning is key to enabling companies to develop AI applications with data coming from a variety of different and decentralized sources while still enabling privacy. For example, AI can learn to offer personalized treatment recommendations for patients based on models trained across patient data from multiple hospitals without sharing sensitive information. Similarly, in the financial sector, customized credit rating systems can be improved by using information from various organizations while safeguarding user confidentiality. Federated learning in the retail industry makes it possible to create highly personalized shopping experiences by examining customer behavior on various platforms. The ability to create personalized, privacy-preserving AI models is driving the increased use of federated learning technology for industries wanting to provide customized services while protecting data in line with the most stringent data protection regimes.

Restraint: High computational requirements for complex federated models

Federated learning requires the training of AI models across many decentralized devices or servers, a process that could be computationally intensive, fundamentally in large complex datasets. Federated learning requires synchronization and aggregation of updates from different sources in real-time, which can pressure existing hardware and resources within a network. This is particularly an issue in industries where the computational infrastructure is limited or for applications such as drug discovery or autonomous systems, which require highly intricate models. The demand for secure and effective communication among nodes increases the computational workload even more. Therefore, the expensive upgrade of infrastructure for federated learning may discourage smaller organizations from embracing this technology, restricting its broad implementation and impeding the market's growth.

Opportunity: Integration of federated learning with blockchain for enhanced security

Blockchain's decentralized and immutable ledger can improve federated learning by securely recording model updates. Federated learning ensures that the data and model parameters exchanged among the participants are tamper-proof. It particularly benefits applications requiring integrity and security of data in industries like finance, healthcare, and retail. In the case of healthcare, the means of blockchain guarantees secure tracking of contributions from different hospitals during federated model training and prevents unauthorized data manipulation. In finance, blockchain ensures that the risk management models, shared across multiple institutions, are transparently and securely updated. Organizations can promote the use of federated learning in security-sensitive applications by integrating blockchain technology, which helps foster trust in collaborative AI models, minimizing data breach risks, and adhering to strict regulatory mandates.

Challenge: Developing robust federated learning algorithms for non-independently and identically distributed (non-IID) data

Non-IID data, where the data distribution varies significantly across different clients or devices, is common in real-world scenarios, such as in healthcare or IoT environments. This variability is often complex for traditional federated learning algorithms to handle, which lowers model efficiency and accuracy. Advancements in algorithms specifically designed to handle non-IID data can unlock the full potential of federated learning. For instance, creating algorithms that efficiently handle heterogeneous patient data from multiple sources can result in more precise diagnosis instruments and treatment suggestions in personalized healthcare. Similarly, in IoT networks, robust algorithms can improve predictive maintenance by accurately analyzing diverse data from different devices and sources. These developments can increase the application of federated learning across industries spurring market expansion and enabling more customized and efficient AI solutions by tackling the problems associated with non-IID data.

Federated Learning Market Ecosystem

Top Companies in Digital Agriculture Market

As per optimistic scenario, among verticals, the automotive and transportation segment to grow at a the highest CAGR during the forecast period

As per optimistic scenario, the automotive and transportation vertical is likely to witness the fastest growth rate in the federated learning market due to the increasing demand for advanced, real-time data processing and decision-making capabilities in autonomous vehicles. Federated learning enables the improvement of these systems through distributed data learning from different vehicles and infrastructures, avoiding the centralization of sensitive information. For instance, self-driving cars can exchange and acquire knowledge from driving data in various locations, improving their skills in handling challenging situations while still protecting privacy. Furthermore, federated learning in smart transportation networks can enhance traffic management by examining data from various sources, enhancing effectiveness and safety. The automotive sector will increasingly utilize federated learning to ensure secure, scalable, and privacy-preserving AI models as it moves towards fully autonomous and connected vehicles, driving market growth in this vertical.

As per optimistic scenario, among regions, Europe to hold the largest market size during the forecast period

Europe is expected to hold the largest market share in the federated learning market due to its stringent data privacy regulations, such as the General Data Protection Regulation (GDPR), which drives demand for decentralized data processing solutions. The region's commitment to safeguarding personal information aligns well with the core principles of federated learning, which keep data close by and lower the chance of security breaches. Europe's advanced healthcare and automotive industries are continuously adopting federated learning for applications such as personalized medicine, drug discovery, and autonomous vehicles. The presence of leading tech firms and research institutions further accelerates innovation and adoption of federated learning across various verticals. In addition, European governments are investing in AI research and development, hence creating a favorable environment for the development of federated learning.

Key Market Players

The federated learning solutions vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgrades, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global federated learning solutions market include NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), along with SMEs and startups such as Owkin (US), Intellegens (UK), Edge Delta (US), Secure AI Labs (US), and Sherpa.AI (Spain).

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Scope of the Report

Report Metrics

Details

Market Size value in 2023

USD 127 million

Market Size value for 2028

USD 210 million

CAGR Growth Rate

10.6%

Largest Market

Europe

Market size available for years

2023–2028

Base year considered

2023

Forecast period

2023–2028

Segments covered

Application, Vertical, and Region

Geographies covered

North America, Europe, APAC, MEA, and Latin America

Companies covered

NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK), Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), Sherpa.AI(Spain), Decentralized Machine Learning(Singapore), Consilient(US), Apheris(Germany), Acuratio(US), FEDML(US).

This research report categorizes the Federated Learning Market based on application, vertical, and region.

Market By Application:

  • Drug Discovery
  • Shopping Experience Personalization
  • Data Privacy and Security Management
  • Risk Management
  • Industrial Internet of Things
  • Online Visual Object Detection
  • Augmented Reality/Virtual Reality
  • Other Applications

Market By Verticals:

  • Banking, Financial Services, and Insurance
  • Healthcare and Life Sciences
  • Retail and Ecommerce
  • Manufacturing
  • Energy and Utilities
  • Automotive and Transportaion
  • IT and Telecommunication
  • Other Verticals

Market By Region:

  • North America
  • Europe
  • APAC
  • MEA
  • Latin America

Recent Developments:

  • In June 2024, Rhino Health announced a partnership with Google Cloud to scale their groundbreaking Federated Computing Solution. Rhino’s Federated Computing Platform (Rhino FCP) unlocks data silos across hyperscalers, data centers, geographies, and organizations.
  • In March 2024, Google Research created a new software library called FAX, which enables large-scale computations for machine learning across various devices, including computers and smartphones. Built on top of JAX, a high-performance ML tool, FAX is specifically designed to simplify the implementation of federated learning.
  • In July 2023, Microsoft announced the release of a system architecture and software development kit (SDK), called the Project Florida. Project Florida aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure and accompanying task management interfaces, as well as multi-platform SDK support.
  • In March 2022, NVIDIA launched Communications Intelligence Platform, a Clara Holoscan, solution which was designed for its healthcare sciences business has been updated to MGX, as a one-of-a-kind end-to-end system for both AI technologies and intelligent healthcare manufacturing and deployment in implantable augmentations.
  • In January 2022, Intel launched OpenVINO integration with TensorFlow, the OpenVINO toolkit is used for online improvements and execution required for increased TensorFlow interoperability. It was created for programmers who want to try out the OpenVINO toolset and see how it might assist them in boosting the effectiveness of existing inferential apps with little code changes.
  • In November 2021, NVIDIA launched NVIDIA FLARE, NVIDIA FLARE stands for Federated Learning Application Runtime Environment is an open-source platform, which is based on the foundation of NVIDIA Clara Train's federated learning software, and was employed for biomedical imagery, functional genomics, cancer, and COVID-19 research. Investigators and data scientists could use this SDK to convert their current ML techniques processes to a decentralized network. NVIDIA FLARE supports a variety of networked topologies, spanning peer-to-peer, asynchronous, and server-client techniques, among others.

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TABLE OF CONTENTS
 
1 INTRODUCTION (Page No. - 21)
    1.1 OBJECTIVES OF THE STUDY 
    1.2 MARKET DEFINITION 
           1.2.1 INCLUSIONS AND EXCLUSIONS
    1.3 MARKET SCOPE 
           1.3.1 MARKET SEGMENTATION
           1.3.2 YEARS CONSIDERED FOR THE STUDY
    1.4 CURRENCY CONSIDERED 
           TABLE 1 UNITED STATES DOLLAR EXCHANGE RATE, 2018–2021
    1.5 STAKEHOLDERS 
    1.6 SUMMARY OF CHANGES 
 
2 RESEARCH METHODOLOGY (Page No. - 25)
           FIGURE 1 FEDERATED LEARNING MARKET: RESEARCH DESIGN
           2.1.1 SECONDARY DATA
           2.1.2 PRIMARY DATA
                    TABLE 2 PRIMARY INTERVIEWS
                    2.1.2.1 Breakup of primary profiles
                    2.1.2.2 Key industry insights
    2.2 MARKET BREAKUP AND DATA TRIANGULATION 
           FIGURE 2 DATA TRIANGULATION
    2.3 MARKET SIZE ESTIMATION 
           FIGURE 3 FEDERATED LEARNING MARKET: MARKET ESTIMATION APPROACH
    2.4 MARKET FORECAST 
           TABLE 3 CRITICAL FACTORS IMPACTING THE MARKET GROWTH
    2.5 ASSUMPTIONS FOR THE STUDY 
    2.6 LIMITATIONS OF THE STUDY 
 
3 EXECUTIVE SUMMARY (Page No. - 35)
    3.1 FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 
           FIGURE 4 GLOBAL FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSAND)
           FIGURE 5 HEALTHCARE AND LIFE SCIENCES VERTICAL TO HOLD THE LARGEST MARKET SHARE DURING THE FORECAST PERIOD
           FIGURE 6 EUROPE TO HOLD THE LARGEST MARKET SHARE BY 2023
    3.2 SUMMARY OF KEY FINDINGS 
 
4 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 40)
    4.1 INTRODUCTION 
    4.2 FEDERATED LEARNING: EVOLUTION 
           FIGURE 7 EVOLUTION OF THE FEDERATED LEARNING MARKET
    4.3 FEDERATED LEARNING: TYPES 
           FIGURE 8 TYPES OF FEDERATED LEARNING
    4.4 FEDERATED LEARNING: ARCHITECTURE 
           FIGURE 9 ARCHITECTURE OF FEDERATED LEARNING
    4.5 MARKET DYNAMICS 
           FIGURE 10 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: FEDERATED LEARNING MARKET
           4.5.1 DRIVERS
                    4.5.1.1 Growing need to increase learning between devices and organizations
                    4.5.1.2 Ability to ensure better data privacy and security by training algorithms on decentralized devices
                    4.5.1.3 Growing adoption of federated learning in various applications for data privacy
                    4.5.1.4 Ability of federated learning to address the difficulty of safeguarding individuals’ anonymity
           4.5.2 RESTRAINTS
                    4.5.2.1 Lack of skilled technical expertise
           4.5.3 OPPORTUNITIES
                    4.5.3.1 Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally
                    4.5.3.2 Capability to enable predictive features on smart devices without impacting the user experience and leaking private information
           4.5.4 CHALLENGES
                    4.5.4.1 Issues of high latency and communication inefficiency
                    4.5.4.2 System integration and interoperability issue
                    4.5.4.3 Indirect information leakage
    4.6 IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING MARKET 
    4.7 ARTIFICIAL INTELLIGENCE: ECOSYSTEM 
           FIGURE 11 ARTIFICIAL INTELLIGENCE ECOSYSTEM
    4.8 USE CASE ANALYSIS 
           4.8.1 WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING
           4.8.2 FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT AND PROTECT PATIENT’S DATA
           4.8.3 WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD
           4.8.4 WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING
           4.8.5 INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA
    4.9 SUPPLY CHAIN ANALYSIS 
           FIGURE 12 SUPPLY CHAIN ANALYSIS
    4.10 PATENT ANALYSIS 
           4.10.1 METHODOLOGY
           4.10.2 DOCUMENT TYPE
                     TABLE 4 PATENTS FILED
           4.10.3 INNOVATION AND PATENT APPLICATIONS
                    FIGURE 13 TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–2021
                    4.10.3.1 Top applicants
                                FIGURE 14 TOP TEN COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–2021
                                TABLE 5 TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING MARKET, 2015–2021
    4.11 TECHNOLOGY ANALYSIS 
           4.11.1 FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING
           4.11.2 FEDERATED LEARNING VS EDGE COMPUTING
           4.11.3 FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS
           4.11.4 FEDERATED LEARNING VS SWARM LEARNING
    4.12 RESEARCH PROJECTS: FEDERATED LEARNING 
           4.12.1 MACHINE LEARNING LEDGER ORCHESTRATION FOR DRUG DISCOVERY (MELLODDY)
                    4.12.1.1 Participants
           4.12.2 FEDAI
           4.12.3 PADDLEPADDLE
           4.12.4 FEATURECLOUD
           4.12.5 MUSKETEER PROJECT
    4.13 REGULATORY LANDSCAPE 
           4.13.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 6 NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 7 EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 8 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 9 REST OF WORLD: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
           4.13.2 REGULATORY IMPLICATIONS AND INDUSTRY STANDARDS
           4.13.3 GENERAL DATA PROTECTION REGULATION
           4.13.4 SEC RULE 17A-4
           4.13.5 ISO/IEC 27001
           4.13.6 SYSTEM AND ORGANIZATION CONTROLS 2 TYPE II COMPLIANCE
           4.13.7 FINANCIAL INDUSTRY REGULATORY AUTHORITY
           4.13.8 FREEDOM OF INFORMATION ACT
           4.13.9 HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT PLAY
    4.14 KEY CONFERENCES AND EVENTS IN 2022 
           TABLE 10 FEDERATED LEARNING MARKET: DETAILED LIST OF CONFERENCES AND EVENTS
    4.15 KEY STAKEHOLDERS AND BUYING CRITERIA 
           4.15.1 KEY STAKEHOLDERS IN THE BUYING PROCESS
                    FIGURE 15 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS
                    TABLE 11 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS (%)
                    TABLE 12 BUYING PROCESS FOR TOP VERTICALS
           4.15.2 BUYING CRITERIA
                    FIGURE 16 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
                    TABLE 13 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
    4.16 TRENDS/DISRUPTIONS IMPACTING BUYERS 
           FIGURE 17 FEDERATED LEARNING MARKET: TRENDS/DISRUPTIONS IMPACTING BUYERS
 
5 FEDERATED LEARNING MARKET, BY APPLICATION (Page No. - 75)
    5.1 INTRODUCTION 
    5.2 DRUG DISCOVERY 
           5.2.1 ABILITY TO ACCELERATE DRUG DISCOVERY BY ENABLING INCREASED COLLABORATIONS FOR FASTER TREATMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
           5.2.2 ASSURANCE OF DATA PRIVACY IS CREATING OPPORTUNITIES FOR FEDERATED LEARNING
    5.3 SHOPPING EXPERIENCE PERSONALIZATION 
           5.3.1 GROWING FOCUS ON ENABLING PERSONALIZED SHOPPING EXPERIENCE WHILE ENSURING CUSTOMER DATA PRIVACY AND NETWORK TRAFFIC REDUCTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
           5.3.2 USE OF FEDERATED LEARNING IN PERSONALIZED RECOMMENDATION
    5.4 DATA PRIVACY AND SECURITY MANAGEMENT 
           5.4.1 FEDERATED LEARNING SOLUTIONS ENABLE BETTER DATA PRIVACY AND SECURITY MANAGEMENT BY LIMITING THE NEED TO MOVE DATA ACROSS NETWORKS BY TRAINING ALGORITHM
           5.4.2 FEDERATED LEARNING HAS EMERGED AS A SOLUTION FOR FACILITATING REMOTE GROUP WORK WHILE KEEPING THE LEARNING DATA PRIVATE
    5.5 RISK MANAGEMENT 
           5.5.1 ABILITY TO ENABLE BFSI ORGANIZATIONS TO COLLABORATE AND LEARN A SHARED PREDICTION MODEL WITHOUT SHARING DATA AND PERFORM EFFICIENT CREDIT RISK ASSESSMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
           5.5.2 FEDERATED MACHINE LEARNING FOR LOAN RISK PREDICTION
    5.6 INDUSTRIAL INTERNET OF THINGS 
           5.6.1 FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA
           5.6.2 BLOCKCHAIN BASED FEDERATED LEARNING SOLUTIONS HELPS IN DEVICE RECOGNITION IN IIOT
    5.7 ONLINE VISUAL OBJECT DETECTION 
           5.7.1 ABILITY TO ENABLE SAFETY MONITORING BY ENHANCED ONLINE VISUAL OBJECT DETECTION FOR SMART CITY APPLICATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
           5.7.2 FEDCV A FRAMEWORK FOR DIVERSE COMPUTER VISION TASKS
    5.8 AUGMENTED REALITY/VIRTUAL REALITY 
           5.8.1 OUTPUT SECURITY FOR MULTI-USER AUGMENTED REALITY USING FEDERATED REINFORCEMENT LEARNING
    5.9 OTHER APPLICATIONS 
 
6 FEDERATED LEARNING MARKET, BY VERTICAL (Page No. - 84)
    6.1 INTRODUCTION 
           TABLE 14 PESSIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
           TABLE 15 AS-IS SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
           TABLE 16 OPTIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
    6.2 BANKING, FINANCIAL SERVICES, AND INSURANCE 
           6.2.1 ABILITY TO REDUCE MALICIOUS ACTIVITIES AND PROTECT CUSTOMER DATA TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS IN THE BFSI VERTICAL
           6.2.2 BANKING, FINANCIAL SERVICES, AND INSURANCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 18 BANKING, FINANCIAL SERVICES, AND INSURANCE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.3 HEALTHCARE AND LIFE SCIENCES 
           6.3.1 LARGE POOL OF APPLICATIONS, MULTIPLE RESEARCH INITIATIVES, AND COLLABORATIONS AMONG TECHNOLOGY VENDORS AND HEALTHCARE AND LIFE SCIENCES ORGANIZATIONS TO DRIVE MARKET GROWTH
           6.3.2 HEALTHCARE AND LIFE SCIENCES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
                    FIGURE 19 HEALTHCARE AND LIFE SCIENCES: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.4 RETAIL AND ECOMMERCE 
           6.4.1 ABILITY TO ENABLE PERSONALIZED CUSTOMER EXPERIENCES WHILE ENSURING CUSTOMER DATA PRIVACY TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE RETAIL AND ECOMMERCE VERTICAL
           6.4.2 RETAIL AND ECOMMERCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 20 RETAIL AND ECOMMERCE: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.5 MANUFACTURING 
           6.5.1 FOCUS ON SMART MANUFACTURING AND NEED FOR ENHANCED OPERATIONAL INTELLIGENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING ACROSS THE MANUFACTURING VERTICAL
           6.5.2 MANUFACTURING: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
                    FIGURE 21 MANUFACTURING: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.6 ENERGY AND UTILITIES 
           6.6.1 NEED TO CONTROL CYBERATTACKS AND IMPROVE POWER GRID RESILIENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE ENERGY AND UTILITIES VERTICAL
           6.6.2 ENERGY AND UTILITIES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
                    FIGURE 22 ENERGY AND UTILITIES: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.7 AUTOMOTIVE AND TRANSPORTATION 
           6.7.1 FEDERATED LEARNING TO RETRAIN THE NETWORK ACROSS NUMEROUS DEVICES IN A DECENTRALIZED MANNER
           6.7.2 AUTOMOTIVE AND TRANSPORTATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 23 AUTOMOTIVE AND TRANSPORTATION: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.8 IT AND TELECOMMUNICATION 
           6.8.1 TRANSFER OF DATA RAISES PRIVACY CONCERNS CAUSING SAFETY AND ECONOMIC DIFFICULTIES
           6.8.2 IT AND TELECOMMUNICATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 24 IT AND TELECOMMUNICATION: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
    6.9 OTHER VERTICALS 
           FIGURE 25 OTHER VERTICALS: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
 
7 FEDERATED LEARNING MARKET, BY REGION (Page No. - 99)
    7.1 INTRODUCTION 
           TABLE 17 PESSIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
           TABLE 18 AS-IS SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
           TABLE 19 OPTIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
    7.2 NORTH AMERICA 
           7.2.1 HIGH FOCUS OF NORTH AMERICAN COMPANIES TOWARD RESEARCH IN FEDERATED LEARNING TO ENABLE FUTURISTIC DATA-TRAINED MODELS
           7.2.2 NORTH AMERICA: FEDERATED LEARNING MARKET DRIVERS
           7.2.3 NORTH AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 26 NORTH AMERICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
           7.2.4 NORTH AMERICA: REGULATIONS
                    7.2.4.1 Health Insurance Portability and Accountability Act of 1996
                    7.2.4.2 California Consumer Privacy Act
                    7.2.4.3 Gramm–Leach–Bliley Act
                    7.2.4.4 Health Information Technology for Economic and Clinical Health Act
                    7.2.4.5 Federal Information Security Management Act
                    7.2.4.6 Payment Card Industry Data Security Standard
                    7.2.4.7 Federal Information Processing Standards
                    7.2.4.8 Sarbanes Oxley Act
                    7.2.4.9 United States Securities and Exchange Commission
    7.3 EUROPE 
           7.3.1 HIGH FOCUS ON DATA PRIVACY AND COMPLIANCE, AND INCREASED RESEARCH COLLABORATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN EUROPE
           7.3.2 EUROPE: FEDERATED LEARNING MARKET DRIVERS
           7.3.3 EUROPE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 27 EUROPE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
           7.3.4 EUROPE: REGULATIONS
                    7.3.4.1 General Data Protection Regulation
                    7.3.4.2 European Committee for Standardization
                    7.3.4.3 European Technical Standards Institute
                    7.3.4.4 European Market Infrastructure Regulation
    7.4 ASIA PACIFIC 
           7.4.1 COUNTRY-WISE FOCUS ON DATA PRIVACY REGULATIONS ALONG WITH THE INCREASING ADOPTION OF EDGE AI AND THE NEED FOR PERSONALIZED SERVICES TO SPUR THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
           7.4.2 ASIA PACIFIC: FEDERATED LEARNING MARKET DRIVERS
           7.4.3 ASIA PACIFIC: FORECAST 2023–2028 (OPTIMISTIC/AS-IS /PESSIMISTIC)
                    FIGURE 28 ASIA PACIFIC: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
           7.4.4 ASIA PACIFIC: REGULATIONS
                    7.4.4.1 Privacy Commissioner for Personal Data
                    7.4.4.2 Act on the Protection of Personal Information
                    7.4.4.3 Critical information infrastructure
                    7.4.4.4 International organization for standardization 27001
                    7.4.4.5 Personal data protection act
    7.5 MIDDLE EAST AND AFRICA 
           7.5.1 STRENGTHENING OF NETWORK INFRASTRUCTURE, GROWING FOOTHOLD OF GLOBAL COMPANIES, AND INCREASING TECHNOLOGY ADOPTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING
           7.5.2 MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET DRIVERS
           7.5.3 MIDDLE EAST AND AFRICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
                    FIGURE 29 MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
           7.5.4 MIDDLE EAST AND AFRICA: REGULATIONS
                    7.5.4.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017
                    7.5.4.2 Cloud Computing Framework
                    7.5.4.3 GDPR applicability in the Kingdom of Saudi Arabia
                    7.5.4.4 Protection of Personal Information Act
    7.6 LATIN AMERICA 
           7.6.1 GROWING ADOPTION OF AI TECHNOLOGY TO DRIVE THE FEDERATED LEARNING MARKET
           7.6.2 LATIN AMERICA: FEDERATED LEARNING MARKET DRIVERS
           7.6.3 LATIN AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/ PESSIMISTIC)
                    FIGURE 30 LATIN AMERICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
           7.6.4 LATIN AMERICA: REGULATIONS
                    7.6.4.1 Brazil Data Protection Law
                    7.6.4.2 Argentina Personal Data Protection Law No. 25.326
                    7.6.4.3 Federal Law on Protection of Personal Data Held by Individuals
 
8 COMPETITIVE LANDSCAPE (Page No. - 117)
    8.1 INTRODUCTION 
           FIGURE 31 MARKET EVALUATION FRAMEWORK
    8.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 
           8.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY FEDERATED LEARNING VENDORS
    8.3 HISTORICAL REVENUE ANALYSIS OF TOP VENDORS 
           FIGURE 32 HISTORICAL REVENUE ANALYSIS
    8.4 COMPETITIVE BENCHMARKING 
           TABLE 20 FEDERATED LEARNING MARKET: NEW LAUNCHES, 2019–2022
           TABLE 21 FEDERATED LEARNING MARKET: DEALS, 2019–2022
 
9 COMPANY PROFILES (Page No. - 126)
(Business Overview, Products Offered, Recent Developments, MnM View Right to win, Strategic choices made, Weaknesses and competitive threats) *  
    9.1 INTRODUCTION 
    9.2 KEY PLAYERS 
           9.2.1 NVIDIA
                    TABLE 22 NVIDIA: BUSINESS OVERVIEW
                    FIGURE 33 NVIDIA: COMPANY SNAPSHOT
                    TABLE 23 NVIDIA: SOLUTIONS OFFERED
                    TABLE 24 NVIDIA: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 25 NVIDIA: DEALS
                    FIGURE 34 BUSINESS MODEL CANVAS: NVIDIA
           9.2.2 GOOGLE
                    TABLE 26 GOOGLE: BUSINESS OVERVIEW
                    FIGURE 35 GOOGLE: COMPANY SNAPSHOT
                    TABLE 27 GOOGLE: SOLUTIONS OFFERED
                    TABLE 28 GOOGLE: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 29 GOOGLE: OTHERS
                    FIGURE 36 BUSINESS MODEL CANVAS: GOOGLE
           9.2.3 MICROSOFT
                    TABLE 30 MICROSOFT: BUSINESS OVERVIEW
                    FIGURE 37 MICROSOFT: COMPANY SNAPSHOT
                    TABLE 31 MICROSOFT: SOLUTIONS OFFERED
                    TABLE 32 MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 33 MICROSOFT: DEALS
                    TABLE 34 MICROSOFT: OTHERS
                    FIGURE 38 BUSINESS MODEL CANVAS: MICROSOFT
           9.2.4 IBM
                    TABLE 35 IBM: BUSINESS OVERVIEW
                    FIGURE 39 IBM: COMPANY SNAPSHOT
                    TABLE 36 IBM: SOLUTIONS OFFERED
                    TABLE 37 IBM: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 38 IBM: DEALS
                    FIGURE 40 BUSINESS MODEL CANVAS: IBM
           9.2.5 CLOUDERA
                    TABLE 39 CLOUDERA: BUSINESS OVERVIEW
                    FIGURE 41 CLOUDERA: COMPANY SNAPSHOT
                    TABLE 40 CLOUDERA: SOLUTIONS OFFERED
                    TABLE 41 CLOUDERA: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 42 CLOUDERA: DEALS
                    FIGURE 42 BUSINESS MODEL CANVAS: CLOUDERA
           9.2.6 INTEL
                    TABLE 43 INTEL: BUSINESS OVERVIEW
                    FIGURE 43 INTEL: COMPANY SNAPSHOT
                    TABLE 44 INTEL: SOLUTIONS OFFERED
                    TABLE 45 INTEL: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 46 INTEL: DEALS
                    TABLE 47 INTEL: OTHERS
           9.2.7 OWKIN
                    TABLE 48 OWKIN: BUSINESS OVERVIEW
                    TABLE 49 OWKIN: SOLUTIONS OFFERED
                    TABLE 50 OWKIN: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 51 OWKIN: DEALS
                    TABLE 52 OWKIN: OTHERS
           9.2.8 INTELLEGENS
                    TABLE 53 INTELLEGENS: BUSINESS OVERVIEW
                    TABLE 54 INTELLEGENS: SOLUTIONS OFFERED
                    TABLE 55 INTELLEGENS: DEALS
                    TABLE 56 INELLEGENS: OTHERS
           9.2.9 EDGE DELTA
                    TABLE 57 EDGE DELTA: BUSINESS OVERVIEW
                    TABLE 58 EDGE DELTA: SOLUTIONS OFFERED
                    TABLE 59 EDGE DELTA: DEALS
                    TABLE 60 EDGE DELTA: OTHERS
           9.2.10 ENVEIL
                    TABLE 61 ENVEIL: BUSINESS OVERVIEW
                    TABLE 62 ENVEIL: SOLUTIONS OFFERED
                    TABLE 63 ENVEIL: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 64 ENVEIL: OTHERS
           9.2.11 LIFEBIT
                    TABLE 65 LIFEBIT: BUSINESS OVERVIEW
                    TABLE 66 LIFEBIT: SOLUTIONS OFFERED
                    TABLE 67 LIFEBIT: PRODUCT LAUNCHES AND ENHANCEMENTS
                    TABLE 68 LIFEBIT: DEALS
                    TABLE 69 LIFEBIT: OTHERS
           9.2.12 DATAFLEETS
                    TABLE 70 DATAFLEETS: BUSINESS OVERVIEW
                    TABLE 71 DATAFLEETS: SOLUTIONS OFFERED
                    TABLE 72 DATAFLEETS: DEALS
                    TABLE 73 DATAFLEETS: OTHERS
    9.3 OTHERS KEY PLAYERS 
           9.3.1 SECURE AI LABS
           9.3.2 SHERPA.AI
           9.3.3 DECENTRALIZED MACHINE LEARNING
           9.3.4 CONSILIENT
           9.3.5 APHERIS
           9.3.6 ACURATIO
           9.3.7 FEDML
*Details on Business Overview, Products Offered, Recent Developments, MnM View, Right to win, Strategic choices made, Weaknesses and competitive threats might not be captured in case of unlisted companies.  
 
10 ADJACENT AND RELATED MARKETS (Page No. - 175)
     10.1 INTRODUCTION 
             10.1.1 RELATED MARKETS
             10.1.2 LIMITATIONS
     10.2 ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2026 
             10.2.1 MARKET DEFINITION
             10.2.2 MARKET OVERVIEW
                        TABLE 74 ARTIFICIAL INTELLIGENCE MARKET SIZE AND GROWTH RATE, 2021–2026 (USD BILLION, Y-O-Y%)
                        10.2.2.1 Artificial intelligence market, by vertical
                                     TABLE 75 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY VERTICAL, 2021–2026 (USD BILLION)
                        10.2.2.2 Artificial intelligence market, by deployment mode
                                     TABLE 76 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY DEPLOYMENT MODE, 2021–2026 (USD BILLION)
                        10.2.2.3 Machine learning market, by organization size
                                     TABLE 77 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY ORGANIZATION SIZE, 2021–2026 (USD BILLION)
                        10.2.2.4 Artificial intelligence market, by service
                                     TABLE 78 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY SERVICE, 2021–2026 (USD BILLION)
                        10.2.2.5 Artificial intelligence market, by region
                                     TABLE 79 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY REGION, 2021–2026 (USD BILLION)
     10.3 MACHINE LEARNING MARKET - GLOBAL FORECAST TO 2022 
             10.3.1 MARKET DEFINITION
             10.3.2 MARKET OVERVIEW
                        TABLE 80 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
                        10.3.2.1 Machine learning market, by vertical
                                     TABLE 81 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
                        10.3.2.2 Machine learning market, by deployment mode
                                     TABLE 82 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
                        10.3.2.3 Machine learning market, by organization size
                                     TABLE 83 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
                        10.3.2.4 Machine learning market, by service
                                     TABLE 84 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
                        10.3.2.5 Machine learning market, by region
                                     TABLE 85 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
     10.4 EDGE AI SOFTWARE MARKET - GLOBAL FORECAST TO 2026 
             10.4.1 MARKET DEFINITION
             10.4.2 MARKET OVERVIEW
                        TABLE 86 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2014–2019 (USD MILLION, Y-O-Y%)
                        TABLE 87 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2019–2026 (USD MILLION, Y-O-Y%)
                        10.4.2.1 Edge AI software market, by component
                                     TABLE 88 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2014–2019 (USD MILLION)
                                     TABLE 89 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2019–2026 (USD MILLION)
                        10.4.2.2 Edge AI software market, by data source
                                     TABLE 90 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2014–2019 (USD MILLION)
                                     TABLE 91 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2019–2026 (USD MILLION)
                        10.4.2.3 Edge AI software market, by application
                                     TABLE 92 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2014–2019 (USD MILLION)
                                     TABLE 93 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2019–2026 (USD MILLION)
                        10.4.2.4 Edge AI software market, by vertical
                                     TABLE 94 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)
                                     TABLE 95 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION)
                        10.4.2.5 Edge AI software market, by region
                                     TABLE 96 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)
                                     TABLE 97 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION)
 
11 APPENDIX (Page No. - 189)
     11.1 DISCUSSION GUIDE 
     11.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     11.3 AVAILABLE CUSTOMIZATIONS 
     11.4 RELATED REPORTS 
     11.5 AUTHOR DETAILS 

The research study for the federated learning market involved the use of extensive secondary sources, directories, and several journals, including Elsevier B.V., IEEE Xplore, and Journal of Medical Internet Research (JMIR), and blogs, such as Google AI, OpenMined, NVIDIA, and IBM, to identify and collect information useful for this comprehensive market research study. Primary sources were industry experts from the core and related industries, preferred federated learning providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information, and assess the market’s prospects.

Secondary Research

In the secondary research process, various sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as Elsevier B.V., IEEE Xplore, and Journal of Medical Internet Research (JMIR), and blogs, such as Google AI, OpenMined, NVIDIA, and IBM, magazines such as Analytics India Magazine, HealthTech magazine, and other magazines.

Primary Research

In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and product development/innovation teams; related key executives from federated learning solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders. Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation.

The following is the breakup of primary profiles:

Federated Learning Market Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

The federated learning market is in an initial stage, with a very limited number of available deployments, and a limited number of vendors. Available secondary data as well as primary information was analyzed to identify use cases, research projects, initiatives, and consortiums specific to the market. An exhaustive list of all vendors offering solutions or having initiatives/research projects in the market was prepared. All players do not have solution offerings, whereas some key players such as Cloudera, IBM, and Google are working on research projects to further explore the potential of the federated learning market. The revenue contribution of the market vendors who have direct offerings was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated on the basis of breadth of applications and verticals. On the other hand, the vendors working on research projects were studied in detail to identify their progress and understand the future scope of federated learning solutions. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation. The list of vendors considered for estimating the market size is not limited to the vendors profiled in the report.

The pricing trend is assumed to vary over time.

  • All the forecasts are made with the standard assumption that the accepted currency is USD.
  • For the conversion of various currencies to USD, average historical exchange rates are used according to the year specified. For all the historical and current exchange rates required for calculations and currency conversions, the US Internal Revenue Service's website is used.
  • All the forecasts are made under the standard assumption that the globally accepted currency USD remains constant during the next five years.

Data Triangulation

After arriving at the overall market size using the market size estimation processes as explained above, . The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation. The list of vendors considered for estimating the market size is not limited to the vendors profiled in the report. However, MarketsandMarkets prepared a laundry list of vendors offering edge AI software and ML solutions, and mapped their products related to the federated learning market to identify major vendors operating in the market. The likelihood of these vendors venturing into market is high as they already have ML and edge AI software-specific offerings and federated learning solutions can enable further efficiencies.

Report Objectives

  • To define, describe, and predict the federated learning market by region
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
  • To analyze opportunities in the market and provide details related to the different vendors operating and working on research projects in the federated learning market
  • To forecast the market size of segments with respect to five main regions: North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America
  • To profile key players and comprehensively analyze their core competencies
  • To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the federated learning market
  • To analyze different applications of federated learning across verticals

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:

Product Analysis

  • Product matrix provides a detailed comparison of the product portfolio of each company

Company Information

  • Detailed analysis and profiling of additional market players up to 5
Custom Market Research Services

We will customize the research for you, in case the report listed above does not meet with your exact requirements. Our custom research will comprehensively cover the business information you require to help you arrive at strategic and profitable business decisions.

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