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

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    Report

  • 195 Pages
  • April 2022
  • Region: Global
  • Markets and Markets
  • ID: 5311706
Description Jump to:
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.


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.

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).


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%, and Latin America: 5%

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

1 Introduction
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, 2018-2020
1.5 Stakeholders
2 Research Methodology
2.1 Research Data
Figure 1 Federated Learning Solutions Market: Research Design
2.1.1 Secondary Data
2.1.2 Primary Data
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 Solutions Market: Market Estimation Approach
2.4 Market Forecast
Table 2 Critical Factors Impacting the Market Growth
2.5 Assumptions for the Study
2.6 Limitations of the Study
3 Executive Summary
3.1 Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 4 Global Federated Learning Solutions Market, 2023-2028 (USD Thousands)
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 in 2023
3.2 Summary of Key Findings
4 Market Overview and Industry Trends
4.1 Introduction
4.2 Federated Learning: Types
Figure 7 Types of Federated Learning
4.3 Federated Learning: Evolution
Figure 8 Evolution of Federated Learning Solutions Market
4.4 Federated Learning: Architecture
Figure 9 Architecture of Federated Learning
4.5 Artificial Intelligence: Ecosystem
Figure 10 Artificial Intelligence Ecosystem
4.6 Research Projects: Federated Learning
4.6.1 Machine Learning Ledger Orchestration for Drug Discovery (MELLODDY)
4.6.1.1 Participants
4.6.2 FEDAI
4.6.3 PaddlePaddle
4.6.4 FeatureCloud
4.6.5 Musketeer Project
4.7 Market Dynamics
Figure 11 Drivers, Restraints, Opportunities, and Challenges: Federated Learning Solutions Market
4.7.1 Drivers
4.7.1.1 Growing Need to Increase Learning Between Devices and Organization
4.7.1.2 Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
4.7.2 Restraints
4.7.2.1 Lack of Skilled Technical Expertise
4.7.3 Opportunities
4.7.3.1 Potential to Enable Companies to Leverage a Shared Ml Model Collaboratively by Keeping Data on Devices
4.7.3.2 Capability to Enable Predictive Features on Smart Devices Without Impacting User Experience and Leaking Private Information
4.7.4 Challenges
4.7.4.1 Issues of High Latency and Communication Inefficiency
4.7.4.2 System Heterogeneity and Issue in Interoperability
4.7.4.3 Indirect Information Leakage
4.8 Impact of Drivers, Restraints, Opportunities, and Challenges on the Federated Learning Solutions Market
4.9 Use Case Analysis
4.9.1 WeBank and a Car Rental Service Provider Enable Insurance Industry to Reduce Data Traffic Violations Through Federated Learning
4.9.2 Federated Learning Enable Healthcare Companies to Encrypt and Protect Patient Data
4.9.3 WeBank and Extreme Vision Introduced Online Visual Object Detection Platform Powered by Federated Learning to Store Data in Cloud
4.9.4 WeBank Introduced Federated Learning Model for Anti-Money Laundering
4.9.5 Intellegens Solution Adoption May Help Clinicals Analyze Heart Rate Data
4.10 Patent Analysis
4.10.1 Methodology
4.10.2 Document Type
Table 3 Patents Filed
4.10.3 Innovation and Patent Applications
Figure 12 Total Number of Patents Granted in a Year, 2015-2021
4.10.3.1 Top Applicants
Figure 13 Top 10 Companies with the Highest Number of Patent Applications, 2015-2021
Table 4 Top Eight Patent Owners (US) in the Federated Learning Solutions Market, 2015-2021
4.11 Supply Chain Analysis
Figure 14 Supply Chain Analysis
4.12 Technology Analysis
4.12.1 Federated Learning vs Distributed Machine Learning
4.12.2 Federated Learning vs Edge Computing
4.12.3 Federated Learning vs Federated Database Systems
4.12.4 Federated Learning vs Swarm Learning
5 Federated Learning Solutions Market, by Application
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
5.3 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
5.4 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
5.5 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
5.6 Industrial Internet of Things
5.6.1 Federated Learning Solutions Enable Predictive Maintenance on Edge Devices Without Centralizing Data and Increase Operational Efficiency
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
5.8 Other Applications
6 Federated Learning Solutions Market, by Vertical
6.1 Introduction
Table 5 Pessimistic Scenario: Market Size, by Vertical, 2023-2028 (USD Thousands)
Table 6 As-Is Scenario: Market Size, by Vertical, 2023-2028 (USD Thousands)
Table 7 Optimistic Scenario: 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 15 Banking, Financial Services, and Insurance: The Federated Learning Solutions 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 16 Healthcare and Life Sciences: The Market, 2023-2028 (USD Thousands)
6.4 Retail and e-Commerce
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 e-Commerce Vertical
6.4.2 Retail and e-Commerce: Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 17 Retail and e-Commerce: The Federated Learning Solutions 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 18 Manufacturing: The 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 19 Energy and Utilities: The Market, 2023-2028 (USD Thousands)
6.7 Other Verticals
7 Federated Learning Solutions Market, by Region
7.1 Introduction
Table 8 Pessimistic Scenario: Market Size, by Region, 2023-2028 (USD Thousands)
Table 9 As-Is Scenario: Market Size, by Region, 2023-2028 (USD Thousands)
Table 10 Optimistic Scenario: 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: Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 20 North America: The Federated Learning Solutions Market, 2023-2028 (USD Thousands)
7.2.3 North America: Regulations
7.2.3.1 Health Insurance Portability and Accountability Act of 1996
7.2.3.2 California Consumer Privacy Act
7.2.3.3 Gramm-Leach-Bliley Act
7.2.3.4 Health Information Technology for Economic and Clinical Health Act
7.2.3.5 Federal Information Security Management Act
7.2.3.6 Payment Card Industry Data Security Standard
7.2.3.7 Federal Information Processing Standards
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: Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 21 Europe: The Federated Learning Solutions Market, 2023-2028 (USD Thousands)
7.3.3 Europe: Regulations
7.3.3.1 General Data Protection Regulation
7.3.3.2 European Committee for Standardization
7.3.3.3 European Technical Standards Institute
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: Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 22 Asia-Pacific: The Federated Learning Solutions Market, 2023-2028 (USD Thousands)
7.4.3 Asia-Pacific: Regulations
7.4.3.1 Privacy Commissioner for Personal Data
7.4.3.2 Act on the Protection of Personal Information
7.4.3.3 Critical Information Infrastructure
7.4.3.4 International Organization for Standardization 27001
7.4.3.5 Personal Data Protection Act
7.5 Rest of World
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 Rest of World: Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
Figure 23 Rest of World: The Federated Learning Solutions Market, 2023-2028 (USD Thousands)
7.5.3 Middle East and Africa: Regulations
7.5.3.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017
7.5.3.2 Cloud Computing Framework
7.5.3.3 GDPR Applicability in the Kingdom of Saudi Arabia (KSA)
7.5.3.4 Protection of Personal Information Act
7.5.4 Latin America: Regulations
7.5.4.1 Brazil Data Protection Law
7.5.4.2 Argentina Personal Data Protection Law No. 25.326
8 Company Profiles
8.1 Introduction
(Business Overview, Solutions, Key Insights, Recent Developments, Analyst's View)*
8.2 NVIDIA
Table 11 NVIDIA: Business Overview
Figure 24 NVIDIA: Company Snapshot
Table 12 NVIDIA: Federated Learning Solutions Market: Solution Launches and Enhancements
Table 13 NVIDIA: Market: Deals
Figure 25 Business Model Canvas: NVIDIA
8.3 Cloudera
Table 14 Cloudera: Business Overview
Figure 26 Cloudera: Company Snapshot
Table 15 Cloudera: Market: Solution Launches and Enhancements
Table 16 Cloudera: Market: Deals
Figure 27 Business Model Canvas: Cloudera
8.4 IBM
Table 17 IBM: Business Overview
Figure 28 IBM: Company Snapshot
Table 18 IBM: Federated Learning Solutions Market: Research Project
Table 19 IBM: Market: Deals
Figure 29 Business Model Canvas: IBM
8.5 Microsoft
Table 20 Microsoft: Business Overview
Figure 30 Microsoft: Company Snapshot
Table 21 Microsoft: Market: Research Project
Table 22 Microsoft: Market: Solution Launches and Enhancements
Table 23 Microsoft: Market: Deals
Figure 31 Business Model Canvas: Microsoft
8.6 Google
Table 24 Google: Business Overview
Figure 32 Google: Company Snapshot
Table 25 Google: Federated Learning Solutions Market: Research Project
Table 26 Google: Market: Solution Launches and Enhancements
Figure 33 Business Model Canvas: Google
8.7 Owkin
Table 27 Owkin: Market: Research Project and Funding
Table 28 Owkin: Market: Deals
8.8 Intellegens
Table 29 Intellegens: Market: Research Project and Funding
8.9 DataFleets
Table 30 DataFleets: Market: Research Project and Funding
Table 31 DataFleets: Market: Deals
8.10 Edge Delta
Table 32 Edge Delta: Market: Research Project and Funding
Table 33 Edge Delta: Market: Deals
8.11 Enveil
Table 34 Enveil: Federated Learning Solutions Market: Research Project and Funding
Table 35 Enveil: Market: Solution Launches and Enhancements
8.12 Lifebit
Table 36 Lifebit: Market: Research Project and Funding
Table 37 Lifebit: Market: Solution Launches and Enhancements
8.13 Secure AI Labs
8.14 Sherpa.ai
8.15 Decentralized Machine Learning
8.16 Consilient
*Details on Business Overview, Solutions, Key Insights, Recent Developments, Analyst's View Might Not be Captured in Case of Unlisted Companies
8.17 Competitive Benchmarking
Table 38 Competitive Benchmarking: Offerings and Regional Presence
Table 39 Competitive Benchmarking: Target Verticals
9 Adjacent and Related Markets
9.1 Introduction
9.2 Machine Learning Market - Global Forecast to 2022
9.2.1 Market Definition
9.2.2 Market Overview
Table 40 Global Machine Learning Market Size and Growth Rate, 2015-2022 (USD Million, Y-O-Y %)
9.2.2.1 Machine Learning Market, by Vertical
Table 41 Machine Learning Market Size, by Vertical, 2015-2022 (USD Million)
9.2.2.2 Machine Learning Market, by Deployment Mode
Table 42 Machine Learning Market Size, by Deployment Mode, 2015-2022 (USD Million)
9.2.2.3 Machine Learning Market, by Organization Size
Table 43 Machine Learning Market Size, by Organization Size, 2015-2022 (USD Million)
9.2.2.4 Machine Learning Market, by Service
Table 44 Machine Learning Market Size, by Service, 2015-2022 (USD Million)
9.2.2.5 Machine Learning Market, by Region
Table 45 Machine Learning Market Size, by Region, 2015-2022 (USD Million)
9.3 Edge AI Software Market - Global Forecast to 2026
9.3.1 Market Definition
9.3.2 Market Overview
Table 46 Global Edge AI Software Market Size and Growth Rate, 2014-2019 (USD Million, Y-O-Y%)
Table 47 Global Edge AI Software Market Size and Growth Rate, 2019-2026 (USD Million, Y-O-Y%)
9.3.2.1 Edge AI Software Market, by Component
Table 48 Edge AI Software Market Size, by Component, 2014-2019 (USD Million)
Table 49 Edge AI Software Market Size, by Component, 2019-2026 (USD Million)
9.3.2.2 Edge AI Software Market, by Data Source
Table 50 Edge AI Software Market Size, by Data Source, 2014-2019 (USD Million)
Table 51 Edge AI Software Market Size, by Data Source, 2019-2026 (USD Million)
9.3.2.3 Edge AI Software Market, by Application
Table 52 Edge AI Software Market Size, by Application, 2014-2019 (USD Million)
Table 53 Edge AI Software Market Size, by Application, 2019-2026 (USD Million)
9.3.2.4 Edge AI Software Market, by Vertical
Table 54 Edge AI Software Market Size, by Vertical, 2014-2019 (USD Million)
Table 55 Edge AI Software Market Size, by Vertical, 2019-2026 (USD Million)
9.3.2.5 Edge AI Software Market, by Region
Table 56 Edge AI Software Market Size, by Region, 2014-2019 (USD Million)
Table 57 Edge AI Software Market Size, by Region, 2019-2026 (USD Million)
10 Appendix
10.1 Industry Experts
10.2 Discussion Guide
10.3 Knowledge Store: The Subscription Portal
10.4 Available Customizations

Companies Mentioned

  • Acuratio
  • Apheris
  • Cloudera
  • Consilient
  • Datafleets
  • Decentralized Machine Learning
  • Edge Delta
  • Enveil
  • FedML
  • Google
  • IBM
  • Intel
  • Intellegens
  • Lifebit
  • Microsoft
  • Nvidia
  • Owkin
  • Secure AI Labs
  • Sherpa.AI
  • WeBank

Methodology

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Table Information

Report AttributeDetails
No. of Pages195
PublishedApril 2022
Forecast Period2023 - 2028
Estimated Market Value ( USD ) in 2023 $ 127 Million
Forecasted Market Value ( USD ) by 2028 $ 210 Million
Compound Annual Growth Rate10.6%
Regions CoveredGlobal
No. of Companies Mentioned20

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