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Federated Learning Solutions Market Upcoming Trends to 2028 - Business Opportunities by Intellegens, DataFleets, Edge Delta, Enveil
iCrowd Newswire
12 Jun 2021, 03:55 GMT+10
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.
Federated Learning Solutions Market Report includes the profiles of Key Industry Players along with their SWOT analysis and market strategies. In addition, the report focuses on leading industry players with information such as company profiles, components and services offered, financial information of last 3 years, key development in past five years.
Federated Learning Solutions Market projected to reach $201 million by 2028, with a remarkable CAGR of 11.4%
iCrowd Newswire
According to a new market research report Federated Learning Solutions Market by Application (Drug Discovery, Industrial IoT), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Retail and eCommerce, Energy and Utilities), and Region - Global Forecast to 2028″ published by MarketsandMarkets, As per AS-IS scenario, the global federated learning solutions market size 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 Machine Learning (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 learnin
4.7.1.2 Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
4.7.2 Restraints
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
4.9 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