Dublin, April 21, 2021 (GLOBE NEWSWIRE) -- The "Global Federated Learning Solutions Market by Application (Drug Discovery, Industrial IoT), Vertical (Healthcare & Life Sciences, BFSI, Manufacturing, Retail & e-Commerce, Energy & Utilities), and Region - Forecast to 2028" report has been added to ResearchAndMarkets.com's offering.
The global federated learning solutions market size is projected to grow from USD 117 million in 2023 to USD 201 million by 2028, at a Compound Annual Growth Rate (CAGR) of 11.4% during the forecast period.
Various factors such as the potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices and the capability to enable predictive features on smart devices without impacting user experience and leaking private information are expected to offer growth opportunities for federated learning solutions during the forecast period.
Among verticals, the manufacturing segment is forecast 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 e-Commerce, energy and utilities, and manufacturing, and other verticals (telecommunications and IT, media and entertainment, and government). The healthcare and life sciences vertical is expected to account for the largest market size during the forecast period. Moreover, the manufacturing vertical is expected to grow at the highest CAGR during the forecast period. With the increasing focus on Industrial Internet of Things (IIoT) and the rise in competition, manufacturing companies are prioritizing the analysis of data collected from numerous sources, including web, mobile, stores, and social media.
Among regions, Asia Pacific (APAC) is projected to grow at the highest CAGR during the forecast period
The federated learning solutions market in APAC is projected to grow at the highest CAGR from 2023 to 2028. The increase in the adoption of emerging technologies, such as big data analytics, AI, and IoT, and ongoing developments to introduce data regulations, as well as focus on hyper-personalization and contextual recommendation in support of budding e-Commerce markets in key countries such as China, India, and Japan are expected to drive the growth of federated learning solutions in the region.
Key Topics Covered:
1 Introduction
2 Research Methodology
3 Executive Summary
3.1 Forecast 2023-2028 (Optimistic/As-Is/Pessimistic)
3.2 Summary of Key Findings
4 Market Overview and Industry Trends
4.1 Introduction
4.2 Federated Learning: Types
4.3 Federated Learning: Evolution
4.4 Federated Learning: Architecture
4.5 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
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 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
4.10.3 Innovation and Patent Applications
4.10.3.1 Top Applicants
4.11 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
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)
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)
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)
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)
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)
6.7 Other Verticals
7 Federated Learning Solutions Market, by Region
7.1 Introduction
7.2 North America
7.3 Europe
7.4 Asia-Pacific
7.5 Rest of World
8 Company Profiles
8.1 Introduction
8.2 NVIDIA
8.3 Cloudera
8.4 IBM
8.5 Microsoft
8.6 Google
8.7 Owkin
8.8 Intellegens
8.9 DataFleets
8.10 Edge Delta
8.11 Enveil
8.12 Lifebit
8.13 Secure AI Labs
8.14 Sherpa.ai
8.15 Decentralized Machine Learning
8.16 Consilient
8.17 Competitive Benchmarking
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
9.2.2.1 Machine Learning Market, by Vertical
9.2.2.2 Machine Learning Market, by Deployment Mode
9.2.2.3 Machine Learning Market, by Organization Size
9.2.2.4 Machine Learning Market, by Service
9.2.2.5 Machine Learning Market, by Region
9.3 Edge AI Software Market - Global Forecast to 2026
9.3.1 Market Definition
9.3.2 Market Overview
9.3.2.1 Edge AI Software Market, by Component
9.3.2.2 Edge AI Software Market, by Data Source
9.3.2.3 Edge AI Software Market, by Application
9.3.2.4 Edge AI Software Market, by Vertical
9.3.2.5 Edge AI Software Market, by Region
10 Appendix
10.1 Industry Experts
10.2 Discussion Guide
10.3 Knowledge Store: The Subscription Portal
10.4 Available Customizations
For more information about this report visit https://www.researchandmarkets.com/r/tz6kr0