Global Federated Learning Market Report 2022: Growing Need to Increase Learning Between Devices and Organizations - Forecast to 2028
Global Federated Learning Market
Dublin, June 10, 2022 (GLOBE NEWSWIRE) -- The "Global 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" report has been added to ResearchAndMarkets.com's offering.
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%
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.
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).
Growing Need to Increase Learning Between Devices and Organizations
Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices
Growing Adoption of Federated Learning in Various Applications for Data Privacy
Ability of Federated Learning to Address the Difficulty of Safeguarding Individuals' Anonymity
Lack of Skilled Technical Expertise
Federated Learning Enables Distributed Participants to Collaboratively Learn a Commonly Shared Model while Holding Data Locally
Capability to Enable Predictive Features on Smart Devices Without Impacting the User Experience and Leaking Private Information
Issues of High Latency and Communication Inefficiency
System Integration and Interoperability Issue
Indirect Information Leakage
Use Case Analysis
WeBank and a Car Rental Service Provider Enable Insurance Industry to Reduce Data Traffic Violations Through Federated Learning
Federated Learning Enable Healthcare Companies to Encrypt and Protect Patient's Data
WeBank and Extreme Vision Introduced Online Visual Object Detection Platform Powered by Federated Learning to Store Data in Cloud
WeBank Introduced Federated Learning Model for Anti-Money Laundering
Intellegens Solution Adoption May Help Clinicals Analyze Heart Rate Data
Federated Learning vs Distributed Machine Learning
Federated Learning vs Edge Computing
Federated Learning vs Federated Database Systems
Federated Learning vs Swarm Learning
Research Projects: Federated Learning
Machine Learning Ledger Orchestration for Drug Discovery (MELLODDY)
Regulatory Bodies, Government Agencies, and Other Organizations
Regulatory Implications and Industry Standards
General Data Protection Regulation
SEC Rule 17A-4
System and Organization Controls 2 Type II Compliance
Financial Industry Regulatory Authority
Freedom of Information Act
Health Insurance Portability and Accountability Act Play
Others Key Players
Secure AI Labs
Decentralized Machine Learning
For more information about this report visit https://www.researchandmarkets.com/r/ugd35w
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