0
     

Report Added
Report already added
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

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

As per AS-IS scenario, the global federated learning market size to grow from USD 127 million in 2023 to USD 210 million by 2028, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period. The major factors including the ability to support enterprises to collaborate on a common machine learning (ML) prototype by keeping information on machines and the power to control predictive features on connected devices without affecting user experience or leaking private information are expected to drive the growth for federated learning solutions.

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

The federated learning solutions market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, automotive and transportation, IT and telecommunications and other verticals (government, and media and entertainment). As per AS-IS scenario, the automotive and transportation vertical is expected to grow at the highest CAGR during the forecast period.

With the introduction of automated vehicles, the focus was on data, edge-to-edge computer technology handling, and improved ML algorithm in addition to making automated vehicles reliable and secure for seamless integration through one area of the globe to another, even as analyzing information and personal confidentiality wirelessly. Effective learning chooses the most relevant pieces of data to classify and add to the instructional pool. Furthermore, they can use federated learning to retrain the network across numerous devices in a decentralized manner using the specific information that we will receive from every car to identify these imperfections and assist in preventing the car from hitting other potholes.

As per AS-IS scenario, among regions, Asia Pacific (APAC) to grow at the highest CAGR during the forecast period

As per AS-IS scenario, the federated learning market in APAC is projected to grow at the highest CAGR from 2023 to 2028. APAC is witnessing an advanced and dynamic adoption of new technologies. Key countries such as India, Japan, Singapore, and China are focusing on implementing regulations for data privacy and security in the coming years. This would create an opportunity to implement federated learning solutions for the security and privacy of data. Many Asian countries are leveraging information-intensive big data technologies and AI to collect data from various data sources. The commercialization of big data, AI, and IoT technologies and the need for further advancements to leverage these technologies to the best is expected to increase adoption in the future.

Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the federated learning market.
By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
By Designation: C-Level Executives: 35%, D-Level Executives: 25%, and Managers: 40%
By Region: APAC: 25%, Europe: 30%, North America: 30%, MEA: 10%, Latin America: 5%
The report includes the study of key players offering federated learning solutions and services. It profiles major vendors in the federated learning market. The major players in the federated learning market include 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), and Sherpa.AI(Spain).

Research Coverage
The market study covers the federated learning market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall federated learning market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
Table of Contents

1INTRODUCTION21
1.1OBJECTIVES OF THE STUDY21
1.2MARKET DEFINITION21
1.2.1INCLUSIONS AND EXCLUSIONS22
1.3MARKET SCOPE23
1.3.1MARKET SEGMENTATION23
1.3.2YEARS CONSIDERED FOR THE STUDY23
1.4CURRENCY CONSIDERED24
TABLE 1UNITED STATES DOLLAR EXCHANGE RATE, 2018–202124
1.5STAKEHOLDERS24
1.6SUMMARY OF CHANGES24
2RESEARCH METHODOLOGY25
2.1RESEARCH DATA25
FIGURE 1FEDERATED LEARNING MARKET: RESEARCH DESIGN25
2.1.1SECONDARY DATA26
2.1.2PRIMARY DATA26
TABLE 2PRIMARY INTERVIEWS26
2.1.2.1Breakup of primary profiles27
2.1.2.2Key industry insights27
2.2MARKET BREAKUP AND DATA TRIANGULATION28
FIGURE 2DATA TRIANGULATION28
2.3MARKET SIZE ESTIMATION29
FIGURE 3FEDERATED LEARNING MARKET: MARKET ESTIMATION APPROACH30
2.4MARKET FORECAST31
TABLE 3CRITICAL FACTORS IMPACTING THE MARKET GROWTH31
2.5ASSUMPTIONS FOR THE STUDY32
2.6LIMITATIONS OF THE STUDY34
3EXECUTIVE SUMMARY35
3.1FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)37
FIGURE 4GLOBAL FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSAND)37
FIGURE 5HEALTHCARE AND LIFE SCIENCES VERTICAL TO HOLD THE LARGEST MARKET SHARE DURING THE FORECAST PERIOD38
FIGURE 6EUROPE TO HOLD THE LARGEST MARKET SHARE BY 202338
3.2SUMMARY OF KEY FINDINGS39

4MARKET OVERVIEW AND INDUSTRY TRENDS40
4.1INTRODUCTION40
4.2FEDERATED LEARNING: EVOLUTION40
FIGURE 7EVOLUTION OF THE FEDERATED LEARNING MARKET40
4.3FEDERATED LEARNING: TYPES41
FIGURE 8TYPES OF FEDERATED LEARNING41
4.4FEDERATED LEARNING: ARCHITECTURE42
FIGURE 9ARCHITECTURE OF FEDERATED LEARNING42
4.5MARKET DYNAMICS43
FIGURE 10DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: FEDERATED LEARNING MARKET43
4.5.1DRIVERS44
4.5.1.1Growing need to increase learning between devices and organizations44
4.5.1.2Ability to ensure better data privacy and security by training algorithms on decentralized devices44
4.5.1.3Growing adoption of federated learning in various applications for data privacy45
4.5.1.4Ability of federated learning to address the difficulty of safeguarding individuals’ anonymity45
4.5.2RESTRAINTS46
4.5.2.1Lack of skilled technical expertise46
4.5.3OPPORTUNITIES47
4.5.3.1Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally47
4.5.3.2Capability to enable predictive features on smart devices without impacting the user experience and leaking private information47
4.5.4CHALLENGES48
4.5.4.1Issues of high latency and communication inefficiency48
4.5.4.2System integration and interoperability issue49
4.5.4.3Indirect information leakage49
4.6IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING MARKET50
4.7ARTIFICIAL INTELLIGENCE: ECOSYSTEM51
FIGURE 11ARTIFICIAL INTELLIGENCE ECOSYSTEM51
4.8USE CASE ANALYSIS52
4.8.1WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING52
4.8.2FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT AND PROTECT PATIENT’S DATA53
4.8.3WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD53
4.8.4WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING54
4.8.5INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA54
4.9SUPPLY CHAIN ANALYSIS55
FIGURE 12SUPPLY CHAIN ANALYSIS55
4.10PATENT ANALYSIS56
4.10.1METHODOLOGY56
4.10.2DOCUMENT TYPE56
TABLE 4PATENTS FILED56
4.10.3INNOVATION AND PATENT APPLICATIONS56
FIGURE 13TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–202156
4.10.3.1Top applicants57
FIGURE 14TOP TEN COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–202157
TABLE 5TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING MARKET, 2015–202157
4.11TECHNOLOGY ANALYSIS58
4.11.1FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING58
4.11.2FEDERATED LEARNING VS EDGE COMPUTING58
4.11.3FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS58
4.11.4FEDERATED LEARNING VS SWARM LEARNING59
4.12RESEARCH PROJECTS: FEDERATED LEARNING59
4.12.1MACHINE LEARNING LEDGER ORCHESTRATION FOR DRUG DISCOVERY (MELLODDY)59
4.12.1.1Participants60
4.12.2FEDAI61
4.12.3PADDLEPADDLE61
4.12.4FEATURECLOUD62
4.12.5MUSKETEER PROJECT62
4.13REGULATORY LANDSCAPE63
4.13.1REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS63
TABLE 6NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS63
TABLE 7EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS64
TABLE 8ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS65
TABLE 9REST OF WORLD: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS66
4.13.2REGULATORY IMPLICATIONS AND INDUSTRY STANDARDS67
4.13.3GENERAL DATA PROTECTION REGULATION67
4.13.4SEC RULE 17A-468
4.13.5ISO/IEC 2700168
4.13.6SYSTEM AND ORGANIZATION CONTROLS 2 TYPE II COMPLIANCE68
4.13.7FINANCIAL INDUSTRY REGULATORY AUTHORITY68
4.13.8FREEDOM OF INFORMATION ACT68
4.13.9HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT PLAY69
4.14KEY CONFERENCES AND EVENTS IN 202269
TABLE 10FEDERATED LEARNING MARKET: DETAILED LIST OF CONFERENCES AND EVENTS69
4.15KEY STAKEHOLDERS AND BUYING CRITERIA71
4.15.1KEY STAKEHOLDERS IN THE BUYING PROCESS71
FIGURE 15INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS71
TABLE 11INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS (%)71
TABLE 12BUYING PROCESS FOR TOP VERTICALS72
4.15.2BUYING CRITERIA73
FIGURE 16KEY BUYING CRITERIA FOR TOP THREE VERTICALS73
TABLE 13KEY BUYING CRITERIA FOR TOP THREE VERTICALS73
4.16TRENDS/DISRUPTIONS IMPACTING BUYERS74
FIGURE 17FEDERATED LEARNING MARKET: TRENDS/DISRUPTIONS IMPACTING BUYERS74
5FEDERATED LEARNING MARKET, BY APPLICATION75
5.1INTRODUCTION76
5.2DRUG DISCOVERY76
5.2.1ABILITY TO ACCELERATE DRUG DISCOVERY BY ENABLING INCREASED COLLABORATIONS FOR FASTER TREATMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS76
5.2.2ASSURANCE OF DATA PRIVACY IS CREATING OPPORTUNITIES FOR FEDERATED LEARNING77
5.3SHOPPING EXPERIENCE PERSONALIZATION77
5.3.1GROWING FOCUS ON ENABLING PERSONALIZED SHOPPING EXPERIENCE WHILE ENSURING CUSTOMER DATA PRIVACY AND NETWORK TRAFFIC REDUCTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS77
5.3.2USE OF FEDERATED LEARNING IN PERSONALIZED RECOMMENDATION78
5.4DATA PRIVACY AND SECURITY MANAGEMENT78
5.4.1FEDERATED LEARNING SOLUTIONS ENABLE BETTER DATA PRIVACY AND SECURITY MANAGEMENT BY LIMITING THE NEED TO MOVE DATA ACROSS NETWORKS BY TRAINING ALGORITHM78
5.4.2FEDERATED LEARNING HAS EMERGED AS A SOLUTION FOR FACILITATING REMOTE GROUP WORK WHILE KEEPING THE LEARNING DATA PRIVATE79
5.5RISK MANAGEMENT79
5.5.1ABILITY 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 SOLUTIONS79
5.5.2FEDERATED MACHINE LEARNING FOR LOAN RISK PREDICTION80
5.6INDUSTRIAL INTERNET OF THINGS80
5.6.1FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA80
5.6.2BLOCKCHAIN BASED FEDERATED LEARNING SOLUTIONS HELPS IN DEVICE RECOGNITION IN IIOT81
5.7ONLINE VISUAL OBJECT DETECTION81
5.7.1ABILITY TO ENABLE SAFETY MONITORING BY ENHANCED ONLINE VISUAL OBJECT DETECTION FOR SMART CITY APPLICATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS81
5.7.2FEDCV A FRAMEWORK FOR DIVERSE COMPUTER VISION TASKS82
5.8AUGMENTED REALITY/VIRTUAL REALITY82
5.8.1OUTPUT SECURITY FOR MULTI-USER AUGMENTED REALITY USING FEDERATED REINFORCEMENT LEARNING82
5.9OTHER APPLICATIONS83
6FEDERATED LEARNING MARKET, BY VERTICAL84
6.1INTRODUCTION85
TABLE 14PESSIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)85
TABLE 15AS-IS SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL,
2023–2028 (USD THOUSANDS)86
TABLE 16OPTIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)86
6.2BANKING, FINANCIAL SERVICES, AND INSURANCE87
6.2.1ABILITY TO REDUCE MALICIOUS ACTIVITIES AND PROTECT CUSTOMER DATA TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS IN THE BFSI VERTICAL87
6.2.2BANKING, FINANCIAL SERVICES, AND INSURANCE: FORECAST
2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)88
FIGURE 18BANKING, FINANCIAL SERVICES, AND INSURANCE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)88
6.3HEALTHCARE AND LIFE SCIENCES88
6.3.1LARGE POOL OF APPLICATIONS, MULTIPLE RESEARCH INITIATIVES, AND COLLABORATIONS AMONG TECHNOLOGY VENDORS AND HEALTHCARE AND LIFE SCIENCES ORGANIZATIONS TO DRIVE MARKET GROWTH88
6.3.2HEALTHCARE AND LIFE SCIENCES: FORECAST 2023–2028 (OPTIMISTIC/
AS-IS/PESSIMISTIC)90
FIGURE 19HEALTHCARE AND LIFE SCIENCES: FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)90
6.4RETAIL AND ECOMMERCE90
6.4.1ABILITY TO ENABLE PERSONALIZED CUSTOMER EXPERIENCES WHILE ENSURING CUSTOMER DATA PRIVACY TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE RETAIL AND ECOMMERCE VERTICAL90
6.4.2RETAIL AND ECOMMERCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)91
FIGURE 20RETAIL AND ECOMMERCE: THE FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)91
6.5MANUFACTURING92
6.5.1FOCUS ON SMART MANUFACTURING AND NEED FOR ENHANCED OPERATIONAL INTELLIGENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING ACROSS THE MANUFACTURING VERTICAL92
6.5.2MANUFACTURING: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)93
FIGURE 21MANUFACTURING: THE FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)93
6.6ENERGY AND UTILITIES93
6.6.1NEED TO CONTROL CYBERATTACKS AND IMPROVE POWER GRID RESILIENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE ENERGY AND UTILITIES VERTICAL93
6.6.2ENERGY AND UTILITIES: FORECAST 2023–2028 (OPTIMISTIC/
AS-IS/PESSIMISTIC)94
FIGURE 22ENERGY AND UTILITIES: THE FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)94
6.7AUTOMOTIVE AND TRANSPORTATION95
6.7.1FEDERATED LEARNING TO RETRAIN THE NETWORK ACROSS NUMEROUS DEVICES IN A DECENTRALIZED MANNER95
6.7.2AUTOMOTIVE AND TRANSPORTATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)96
FIGURE 23AUTOMOTIVE AND TRANSPORTATION: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)96
6.8IT AND TELECOMMUNICATION96
6.8.1TRANSFER OF DATA RAISES PRIVACY CONCERNS CAUSING SAFETY AND ECONOMIC DIFFICULTIES96
6.8.2IT AND TELECOMMUNICATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)97
FIGURE 24IT AND TELECOMMUNICATION: THE FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)97
6.9OTHER VERTICALS98
FIGURE 25OTHER VERTICALS: THE FEDERATED LEARNING MARKET,
2023–2028 (USD THOUSANDS)98
7FEDERATED LEARNING MARKET, BY REGION99
7.1INTRODUCTION100
TABLE 17PESSIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)100
TABLE 18AS-IS SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION,
2023–2028 (USD THOUSANDS)100
TABLE 19OPTIMISTIC SCENARIO: FEDERATED LEARNING MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)101
7.2NORTH AMERICA101
7.2.1HIGH FOCUS OF NORTH AMERICAN COMPANIES TOWARD RESEARCH IN FEDERATED LEARNING TO ENABLE FUTURISTIC DATA-TRAINED MODELS101
7.2.2NORTH AMERICA: FEDERATED LEARNING MARKET DRIVERS102
7.2.3NORTH AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)102
FIGURE 26NORTH AMERICA: FEDERATED LEARNING MARKET, 2023–2028
(USD THOUSANDS)102
7.2.4NORTH AMERICA: REGULATIONS103
7.2.4.1Health Insurance Portability and Accountability Act of 1996103
7.2.4.2California Consumer Privacy Act103
7.2.4.3Gramm–Leach–Bliley Act103
7.2.4.4Health Information Technology for Economic and Clinical Health Act103
7.2.4.5Federal Information Security Management Act104
7.2.4.6Payment Card Industry Data Security Standard104
7.2.4.7Federal Information Processing Standards104
7.2.4.8Sarbanes Oxley Act105
7.2.4.9United States Securities and Exchange Commission105
7.3EUROPE105
7.3.1HIGH FOCUS ON DATA PRIVACY AND COMPLIANCE, AND INCREASED RESEARCH COLLABORATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN EUROPE105
7.3.2EUROPE: FEDERATED LEARNING MARKET DRIVERS106
7.3.3EUROPE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)107
FIGURE 27EUROPE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)107
7.3.4EUROPE: REGULATIONS107
7.3.4.1General Data Protection Regulation107
7.3.4.2European Committee for Standardization108
7.3.4.3European Technical Standards Institute108
7.3.4.4European Market Infrastructure Regulation108
7.4ASIA PACIFIC108
7.4.1COUNTRY-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 SOLUTIONS108
7.4.2ASIA PACIFIC: FEDERATED LEARNING MARKET DRIVERS109
7.4.3ASIA PACIFIC: FORECAST 2023–2028 (OPTIMISTIC/AS-IS /PESSIMISTIC)110
FIGURE 28ASIA PACIFIC: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)110
7.4.4ASIA PACIFIC: REGULATIONS110
7.4.4.1Privacy Commissioner for Personal Data110
7.4.4.2Act on the Protection of Personal Information110
7.4.4.3Critical information infrastructure111
7.4.4.4International organization for standardization 27001111
7.4.4.5Personal data protection act111
7.5MIDDLE EAST AND AFRICA112
7.5.1STRENGTHENING OF NETWORK INFRASTRUCTURE, GROWING FOOTHOLD OF GLOBAL COMPANIES, AND INCREASING TECHNOLOGY ADOPTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING112
7.5.2MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET DRIVERS112
7.5.3MIDDLE EAST AND AFRICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)113
FIGURE 29MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)113
7.5.4MIDDLE EAST AND AFRICA: REGULATIONS113
7.5.4.1Israeli Privacy Protection Regulations (Data Security), 5777-2017113
7.5.4.2Cloud Computing Framework113
7.5.4.3GDPR applicability in the Kingdom of Saudi Arabia114
7.5.4.4Protection of Personal Information Act114
7.6LATIN AMERICA114
7.6.1GROWING ADOPTION OF AI TECHNOLOGY TO DRIVE THE FEDERATED LEARNING MARKET114
7.6.2LATIN AMERICA: FEDERATED LEARNING MARKET DRIVERS115
7.6.3LATIN AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/ PESSIMISTIC)115
FIGURE 30LATIN AMERICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)115
7.6.4LATIN AMERICA: REGULATIONS115
7.6.4.1Brazil Data Protection Law115
7.6.4.2Argentina Personal Data Protection Law No. 25.326116
7.6.4.3Federal Law on Protection of Personal Data Held by Individuals116
8COMPETITIVE LANDSCAPE117
8.1INTRODUCTION117
FIGURE 31MARKET EVALUATION FRAMEWORK117
8.2KEY PLAYER STRATEGIES/RIGHT TO WIN117
8.2.1OVERVIEW OF STRATEGIES ADOPTED BY KEY FEDERATED LEARNING VENDORS117
8.3HISTORICAL REVENUE ANALYSIS OF TOP VENDORS119
FIGURE 32HISTORICAL REVENUE ANALYSIS119
8.4COMPETITIVE BENCHMARKING120
TABLE 20FEDERATED LEARNING MARKET: NEW LAUNCHES, 2019–2022120
TABLE 21FEDERATED LEARNING MARKET: DEALS, 2019–2022122
9COMPANY PROFILES126
(Business Overview, Products Offered, Recent Developments, MnM View Right to win, Strategic choices made, Weaknesses and competitive threats) *
9.1INTRODUCTION126
9.2KEY PLAYERS126
9.2.1NVIDIA126
TABLE 22NVIDIA: BUSINESS OVERVIEW126
FIGURE 33NVIDIA: COMPANY SNAPSHOT127
TABLE 23NVIDIA: SOLUTIONS OFFERED128
TABLE 24NVIDIA: PRODUCT LAUNCHES AND ENHANCEMENTS128
TABLE 25NVIDIA: DEALS130
FIGURE 34BUSINESS MODEL CANVAS: NVIDIA130
9.2.2GOOGLE132
TABLE 26GOOGLE: BUSINESS OVERVIEW132
FIGURE 35GOOGLE: COMPANY SNAPSHOT133
TABLE 27GOOGLE: SOLUTIONS OFFERED134
TABLE 28GOOGLE: PRODUCT LAUNCHES AND ENHANCEMENTS134
TABLE 29GOOGLE: OTHERS134
FIGURE 36BUSINESS MODEL CANVAS: GOOGLE135
9.2.3MICROSOFT136
TABLE 30MICROSOFT: BUSINESS OVERVIEW136
FIGURE 37MICROSOFT: COMPANY SNAPSHOT137
TABLE 31MICROSOFT: SOLUTIONS OFFERED138
TABLE 32MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS138
TABLE 33MICROSOFT: DEALS138
TABLE 34MICROSOFT: OTHERS139
FIGURE 38BUSINESS MODEL CANVAS: MICROSOFT140
9.2.4IBM141
TABLE 35IBM: BUSINESS OVERVIEW141
FIGURE 39IBM: COMPANY SNAPSHOT142
TABLE 36IBM: SOLUTIONS OFFERED143
TABLE 37IBM: PRODUCT LAUNCHES AND ENHANCEMENTS143
TABLE 38IBM: DEALS144
FIGURE 40BUSINESS MODEL CANVAS: IBM145
9.2.5CLOUDERA146
TABLE 39CLOUDERA: BUSINESS OVERVIEW146
FIGURE 41CLOUDERA: COMPANY SNAPSHOT147
TABLE 40CLOUDERA: SOLUTIONS OFFERED148
TABLE 41CLOUDERA: PRODUCT LAUNCHES AND ENHANCEMENTS148
TABLE 42CLOUDERA: DEALS148
FIGURE 42BUSINESS MODEL CANVAS: CLOUDERA149
9.2.6INTEL151
TABLE 43INTEL: BUSINESS OVERVIEW151
FIGURE 43INTEL: COMPANY SNAPSHOT152
TABLE 44INTEL: SOLUTIONS OFFERED152
TABLE 45INTEL: PRODUCT LAUNCHES AND ENHANCEMENTS153
TABLE 46INTEL: DEALS153
TABLE 47INTEL: OTHERS154
9.2.7OWKIN155
TABLE 48OWKIN: BUSINESS OVERVIEW155
TABLE 49OWKIN: SOLUTIONS OFFERED155
TABLE 50OWKIN: PRODUCT LAUNCHES AND ENHANCEMENTS156
TABLE 51OWKIN: DEALS156
TABLE 52OWKIN: OTHERS157
9.2.8INTELLEGENS159
TABLE 53INTELLEGENS: BUSINESS OVERVIEW159
TABLE 54INTELLEGENS: SOLUTIONS OFFERED159
TABLE 55INTELLEGENS: DEALS160
TABLE 56INELLEGENS: OTHERS160
9.2.9EDGE DELTA161
TABLE 57EDGE DELTA: BUSINESS OVERVIEW161
TABLE 58EDGE DELTA: SOLUTIONS OFFERED161
TABLE 59EDGE DELTA: DEALS161
TABLE 60EDGE DELTA: OTHERS162
9.2.10ENVEIL163
TABLE 61ENVEIL: BUSINESS OVERVIEW163
TABLE 62ENVEIL: SOLUTIONS OFFERED163
TABLE 63ENVEIL: PRODUCT LAUNCHES AND ENHANCEMENTS164
TABLE 64ENVEIL: OTHERS164
9.2.11LIFEBIT165
TABLE 65LIFEBIT: BUSINESS OVERVIEW165
TABLE 66LIFEBIT: SOLUTIONS OFFERED165
TABLE 67LIFEBIT: PRODUCT LAUNCHES AND ENHANCEMENTS166
TABLE 68LIFEBIT: DEALS166
TABLE 69LIFEBIT: OTHERS167
9.2.12DATAFLEETS168
TABLE 70DATAFLEETS: BUSINESS OVERVIEW168
TABLE 71DATAFLEETS: SOLUTIONS OFFERED168
TABLE 72DATAFLEETS: DEALS169
TABLE 73DATAFLEETS: OTHERS169
9.3OTHERS KEY PLAYERS170
9.3.1SECURE AI LABS170
9.3.2SHERPA.AI171
9.3.3DECENTRALIZED MACHINE LEARNING171
9.3.4CONSILIENT172
9.3.5APHERIS173
9.3.6ACURATIO173
9.3.7FEDML174
*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.
10ADJACENT AND RELATED MARKETS175
10.1INTRODUCTION175
10.1.1RELATED MARKETS175
10.1.2LIMITATIONS175
10.2ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2026176
10.2.1MARKET DEFINITION176
10.2.2MARKET OVERVIEW176
TABLE 74ARTIFICIAL INTELLIGENCE MARKET SIZE AND GROWTH RATE,
2021–2026 (USD BILLION, Y-O-Y%)176
10.2.2.1Artificial intelligence market, by vertical176
TABLE 75ARTIFICIAL INTELLIGENCE MARKET SIZE, BY VERTICAL,
2021–2026 (USD BILLION)177
10.2.2.2Artificial intelligence market, by deployment mode177
TABLE 76ARTIFICIAL INTELLIGENCE MARKET SIZE, BY DEPLOYMENT MODE,
2021–2026 (USD BILLION)177
10.2.2.3Machine learning market, by organization size178
TABLE 77ARTIFICIAL INTELLIGENCE MARKET SIZE, BY ORGANIZATION SIZE,
2021–2026 (USD BILLION)178
10.2.2.4Artificial intelligence market, by service178
TABLE 78ARTIFICIAL INTELLIGENCE MARKET SIZE, BY SERVICE,
2021–2026 (USD BILLION)178
10.2.2.5Artificial intelligence market, by region179
TABLE 79ARTIFICIAL INTELLIGENCE MARKET SIZE, BY REGION,
2021–2026 (USD BILLION)179
10.3MACHINE LEARNING MARKET - GLOBAL FORECAST TO 2022179
10.3.1MARKET DEFINITION179
10.3.2MARKET OVERVIEW179
TABLE 80GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE,
2015–2022 (USD MILLION, Y-O-Y %)180
10.3.2.1Machine learning market, by vertical180
TABLE 81MACHINE LEARNING MARKET SIZE, BY VERTICAL,
2015–2022 (USD MILLION)180
10.3.2.2Machine learning market, by deployment mode181
TABLE 82MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE,
2015–2022 (USD MILLION)181
10.3.2.3Machine learning market, by organization size181
TABLE 83MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE,
2015–2022 (USD MILLION)181
10.3.2.4Machine learning market, by service182
TABLE 84MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)182
10.3.2.5Machine learning market, by region182
TABLE 85MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)182
10.4EDGE AI SOFTWARE MARKET - GLOBAL FORECAST TO 2026183
10.4.1MARKET DEFINITION183
10.4.2MARKET OVERVIEW183
TABLE 86GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE,
2014–2019 (USD MILLION, Y-O-Y%)183
TABLE 87GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE,
2019–2026 (USD MILLION, Y-O-Y%)184
10.4.2.1Edge AI software market, by component184
TABLE 88EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT,
2014–2019 (USD MILLION)184
TABLE 89EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT,
2019–2026 (USD MILLION)184
10.4.2.2Edge AI software market, by data source184
TABLE 90EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE,
2014–2019 (USD MILLION)185
TABLE 91EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE,
2019–2026 (USD MILLION)185
10.4.2.3Edge AI software market, by application185
TABLE 92EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION,
2014–2019 (USD MILLION)186
TABLE 93EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION,
2019–2026 (USD MILLION)186
10.4.2.4Edge AI software market, by vertical186
TABLE 94EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)187
TABLE 95EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION)187
10.4.2.5Edge AI software market, by region187
TABLE 96EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)188
TABLE 97EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION)188
11APPENDIX189
11.1DISCUSSION GUIDE189
11.2KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL193
11.3AVAILABLE CUSTOMIZATIONS195
11.4RELATED REPORTS195
11.5AUTHOR DETAILS196

Report Title: 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


Your Details
Valid Invalid number

SELECT A FORMAT

ADD TO CART BUY NOW