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).
Market Dynamics
Drivers
- 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
Restraints
- Lack of Skilled Technical Expertise
Opportunities
- 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
Challenges
- 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
Technology Analysis
- 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)
- Participants
- FedAI
- PaddlePaddle
- FeatureCloud
- Musketeer Project
Regulatory Landscape
- Regulatory Bodies, Government Agencies, and Other Organizations
- Regulatory Implications and Industry Standards
- General Data Protection Regulation
- SEC Rule 17A-4
- ISO/IEC 27001
- System and Organization Controls 2 Type II Compliance
- Financial Industry Regulatory Authority
- Freedom of Information Act
- Health Insurance Portability and Accountability Act Play
Company Profiles
Key Players
- Nvidia
- Microsoft
- IBM
- Cloudera
- Intel
- Owkin
- Intellegens
- Edge Delta
- Enveil
- Lifebit
- Datafleets
Others Key Players
- Secure AI Labs
- Sherpa.AI
- Decentralized Machine Learning
- Consilient
- Apheris
- Acuratio
- FedML
For more information about this report visit https://www.researchandmarkets.com/r/ugd35w
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