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Global Federated Learning Solutions Market by Federal Learning Types (Centralized, Decentralized, Heterogeneous), Vertical (Banking, Financial Services, & Insurance, Energy & Utilities, Healthcare & Life Sciences), Application - Forecast 2023-2030

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  • 198 Pages
  • March 2024
  • Region: Global
  • 360iResearch™
  • ID: 5322977
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The Federated Learning Solutions Market size was estimated at USD 125.68 million in 2022, USD 144.55 million in 2023, and is expected to grow at a CAGR of 15.19% to reach USD 389.74 million by 2030.

The federated learning solutions market is an emerging and rapidly growing domain with a broader field of artificial intelligence, machine learning, and data privacy. The federated learning solutions deals with collaborative learning models that enable multiple data-owning organizations to train machine learning algorithms on their respective datasets without sharing or transferring raw data. The increasing focus on IIoT with advances in machine learning is contributing to cater to the rising need for learning between devices & organizations, fueling the market growth. The enhanced technological abilities of organizations ensure better data privacy by training algorithms on decentralized devices, increasing the need for federated learning solutions. However, a lack of skilled technical expertise may limit the market adoption of federated learning solutions. The technological issues related to the high latency and communication inefficiency are also creating challenges in the market. Moreover, the rising potential of organizations to leverage shared ML models by storing data on devices could enhance the market adoption of federated learning solutions. The increasing capabilities of organizations to enable predictive features on smart devices are also expected to create lucrative opportunities for market growth.

Regional Insights

The Americas has a highly developed infrastructure for the federated learning solutions market due to the strong presence of significant market players and increased digitization in the region. The United States and Canada are at the forefront of technological advancements in federated learning solutions with strong research and development ecosystems backed by public and private investments. European countries have strict government regulations related to data protection and user privacy in developing and implementing distributed machine learning models across various devices, data sources, and organizations. The Middle region has a rising scope in federated learning solutions due to enhanced adoption of machine learning solutions in smart city projects. The APAC region economies such as China, Japan, and India are investing in rapid technological advancement in federated learning solutions. The governments in the region have been actively funding research initiatives and fostering collaboration between academia and industry to drive innovation in the market.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Federated Learning Solutions Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Federated Learning Solutions Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Federated Learning Solutions Market, highlighting leading vendors and their innovative profiles. These include Acuratio Inc., apheris AI GmbH, Aptima, Inc., BranchKey B.V., Cloudera, Inc., Consilient, Duality Technologies Inc., Edge Delta, Inc., Ekkono Solutions AB, Enveil, Inc., Everest Global, Inc., Faculty Science Limited, FedML, Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Integral and Open Systems, Inc., Intel Corporation, Intellegens Limited, International Business Machines Corporation, Lifebit Biotech Ltd., LiveRamp Holdings, Inc., Microsoft Corporation, Nvidia Corporation, Oracle Corporation, Owkin Inc., SAP SE, Secure AI Labs, Sherpa Europe S.L., SoulPage IT Solutions, TripleBlind, WeBank Co., Ltd., and Zoho Corporation Pvt. Ltd.

Market Segmentation & Coverage

This research report categorizes the Federated Learning Solutions Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Federal Learning Types
    • Centralized
    • Decentralized
    • Heterogeneous
  • Vertical
    • Banking, Financial Services, & Insurance
    • Energy & Utilities
    • Healthcare & Life Sciences
    • Manufacturing
    • Retail & e-Commerce
  • Application
    • Data Privacy & Security Management
    • Drug Discovery
    • Industrial Internet of Things
    • Online Visual Object Detection
    • Risk Management
    • Shopping Experience Personalization
  • Region
    • Americas
      • Argentina
      • Brazil
      • Canada
      • Mexico
      • United States
        • California
        • Florida
        • Illinois
        • New York
        • Ohio
        • Pennsylvania
        • Texas
    • Asia-Pacific
      • Australia
      • China
      • India
      • Indonesia
      • Japan
      • Malaysia
      • Philippines
      • Singapore
      • South Korea
      • Taiwan
      • Thailand
      • Vietnam
    • Europe, Middle East & Africa
      • Denmark
      • Egypt
      • Finland
      • France
      • Germany
      • Israel
      • Italy
      • Netherlands
      • Nigeria
      • Norway
      • Poland
      • Qatar
      • Russia
      • Saudi Arabia
      • South Africa
      • Spain
      • Sweden
      • Switzerland
      • Turkey
      • United Arab Emirates
      • United Kingdom

The report offers valuable insights on the following aspects

  1. Market Penetration: It presents comprehensive information on the market provided by key players.
  2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
  3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
  4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
  5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as

  1. What is the market size and forecast of the Federated Learning Solutions Market?
  2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Federated Learning Solutions Market?
  3. What are the technology trends and regulatory frameworks in the Federated Learning Solutions Market?
  4. What is the market share of the leading vendors in the Federated Learning Solutions Market?
  5. Which modes and strategic moves are suitable for entering the Federated Learning Solutions Market?

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This report also includes a complimentary Excel file with data from the report for purchasers at the Site License or greater level.

Table of Contents

1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Limitations
1.7. Assumptions
1.8. Stakeholders
2. Research Methodology
2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update
3. Executive Summary
4. Market Overview
4.1. Introduction
4.2. Federated Learning Solutions Market, by Region
5. Market Insights
5.1. Market Dynamics
5.1.1. Drivers
5.1.1.1. Increasing Need for Learning between Device & Organisation
5.1.1.2. Increasing Focus on IIOt with Advances in Machine Learning
5.1.1.3. Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
5.1.2. Restraints
5.1.2.1. Lack of Skilled Technical Expertise
5.1.3. Opportunities
5.1.3.1. Organization's Potential to Leverage Shared ML Model by Storing Data on Device
5.1.3.2. Capability to Enable Predictive Features on Smart Devices without Impacting User Experience and Privacy
5.1.4. Challenges
5.1.4.1. Issue of High Latency and Communication Inefficiency
5.2. Market Segmentation Analysis
5.2.1. Types: Techniques for training machine learning models while preserving data privacy
5.2.2. Vertical: Need-based preference for federated learning solutions across diverse industries
5.2.3. Application: Significance of federated learning solutions for wide scope of applications
5.3. Market Trend Analysis
5.4. Cumulative Impact of COVID-19
5.5. Cumulative Impact of Russia-Ukraine Conflict
5.6. Cumulative Impact of High Inflation
5.7. Porter’s Five Forces Analysis
5.7.1. Threat of New Entrants
5.7.2. Threat of Substitutes
5.7.3. Bargaining Power of Customers
5.7.4. Bargaining Power of Suppliers
5.7.5. Industry Rivalry
5.8. Value Chain & Critical Path Analysis
5.9. Regulatory Framework
5.10. Client Customization
6. Federated Learning Solutions Market, by Federal Learning Types
6.1. Introduction
6.2. Centralized
6.3. Decentralized
6.4. Heterogeneous
7. Federated Learning Solutions Market, by Vertical
7.1. Introduction
7.2. Banking, Financial Services, & Insurance
7.3. Energy & Utilities
7.4. Healthcare & Life Sciences
7.5. Manufacturing
7.6. Retail & e-Commerce
8. Federated Learning Solutions Market, by Application
8.1. Introduction
8.2. Data Privacy & Security Management
8.3. Drug Discovery
8.4. Industrial Internet of Things
8.5. Online Visual Object Detection
8.6. Risk Management
8.7. Shopping Experience Personalization
9. Americas Federated Learning Solutions Market
9.1. Introduction
9.2. Argentina
9.3. Brazil
9.4. Canada
9.5. Mexico
9.6. United States
10. Asia-Pacific Federated Learning Solutions Market
10.1. Introduction
10.2. Australia
10.3. China
10.4. India
10.5. Indonesia
10.6. Japan
10.7. Malaysia
10.8. Philippines
10.9. Singapore
10.10. South Korea
10.11. Taiwan
10.12. Thailand
10.13. Vietnam
11. Europe, Middle East & Africa Federated Learning Solutions Market
11.1. Introduction
11.2. Denmark
11.3. Egypt
11.4. Finland
11.5. France
11.6. Germany
11.7. Israel
11.8. Italy
11.9. Netherlands
11.10. Nigeria
11.11. Norway
11.12. Poland
11.13. Qatar
11.14. Russia
11.15. Saudi Arabia
11.16. South Africa
11.17. Spain
11.18. Sweden
11.19. Switzerland
11.20. Turkey
11.21. United Arab Emirates
11.22. United Kingdom
12. Competitive Landscape
12.1. FPNV Positioning Matrix
12.2. Market Share Analysis, By Key Player
12.3. Competitive Scenario Analysis, By Key Player
12.3.1. Agreement, Collaboration, & Partnership
12.3.1.1. FedML Announces Partnership with Theta Network to Empower Collaborative Machine Learning for Generative AI and Ad Recommendation
12.3.2. New Product Launch & Enhancement
12.3.2.1. Consilient Brings to Market its Next-Generation Federated Learning Solution for Financial Crime Detection
12.3.3. Investment & Funding
12.3.3.1. EIC Grants Ekkono Solutions €2.5 Million in Funding for Federated Learning Software Development
13. Competitive Portfolio
13.1. Key Company Profiles
13.1.1. Acuratio Inc.
13.1.2. apheris AI GmbH
13.1.3. Aptima, Inc.
13.1.4. BranchKey B.V.
13.1.5. Cloudera, Inc.
13.1.6. Consilient
13.1.7. Duality Technologies Inc.
13.1.8. Edge Delta, Inc.
13.1.9. Ekkono Solutions AB
13.1.10. Enveil, Inc.
13.1.11. Everest Global, Inc.
13.1.12. Faculty Science Limited
13.1.13. FedML
13.1.14. Google LLC by Alphabet Inc.
13.1.15. Hewlett Packard Enterprise Development LP
13.1.16. Integral and Open Systems, Inc.
13.1.17. Intel Corporation
13.1.18. Intellegens Limited
13.1.19. International Business Machines Corporation
13.1.20. Lifebit Biotech Ltd.
13.1.21. LiveRamp Holdings, Inc.
13.1.22. Microsoft Corporation
13.1.23. Nvidia Corporation
13.1.24. Oracle Corporation
13.1.25. Owkin Inc.
13.1.26. SAP SE
13.1.27. Secure AI Labs
13.1.28. Sherpa Europe S.L.
13.1.29. SoulPage IT Solutions
13.1.30. TripleBlind
13.1.31. WeBank Co., Ltd.
13.1.32. Zoho Corporation Pvt. Ltd.
13.2. Key Product Portfolio
14. Appendix
14.1. Discussion Guide
14.2. License & Pricing
List of Figures
FIGURE 1. FEDERATED LEARNING SOLUTIONS MARKET RESEARCH PROCESS
FIGURE 2. FEDERATED LEARNING SOLUTIONS MARKET SIZE, 2022 VS 2030
FIGURE 3. FEDERATED LEARNING SOLUTIONS MARKET SIZE, 2018-2030 (USD MILLION)
FIGURE 4. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY REGION, 2022 VS 2030 (%)
FIGURE 5. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY REGION, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 6. FEDERATED LEARNING SOLUTIONS MARKET DYNAMICS
FIGURE 7. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2022 VS 2030 (%)
FIGURE 8. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY FEDERAL LEARNING TYPES, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 9. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2022 VS 2030 (%)
FIGURE 10. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 11. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2022 VS 2030 (%)
FIGURE 12. FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY APPLICATION, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 13. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2030 (%)
FIGURE 14. AMERICAS FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 15. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY STATE, 2022 VS 2030 (%)
FIGURE 16. UNITED STATES FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY STATE, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 17. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2030 (%)
FIGURE 18. ASIA-PACIFIC FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 19. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2030 (%)
FIGURE 20. EUROPE, MIDDLE EAST & AFRICA FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY COUNTRY, 2022 VS 2023 VS 2030 (USD MILLION)
FIGURE 21. FEDERATED LEARNING SOLUTIONS MARKET, FPNV POSITIONING MATRIX, 2022
FIGURE 22. FEDERATED LEARNING SOLUTIONS MARKET SHARE, BY KEY PLAYER, 2022

Companies Mentioned

  • Acuratio Inc.
  • apheris AI GmbH
  • Aptima, Inc.
  • BranchKey B.V.
  • Cloudera, Inc.
  • Consilient
  • Duality Technologies Inc.
  • Edge Delta, Inc.
  • Ekkono Solutions AB
  • Enveil, Inc.
  • Everest Global, Inc.
  • Faculty Science Limited
  • FedML
  • Google LLC by Alphabet Inc.
  • Hewlett Packard Enterprise Development LP
  • Integral and Open Systems, Inc.
  • Intel Corporation
  • Intellegens Limited
  • International Business Machines Corporation
  • Lifebit Biotech Ltd.
  • LiveRamp Holdings, Inc.
  • Microsoft Corporation
  • Nvidia Corporation
  • Oracle Corporation
  • Owkin Inc.
  • SAP SE
  • Secure AI Labs
  • Sherpa Europe S.L.
  • SoulPage IT Solutions
  • TripleBlind
  • WeBank Co., Ltd.
  • Zoho Corporation Pvt. Ltd.

Methodology

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Table Information