DUBLIN–(BUSINESS WIRE)–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.
Market Dynamics
Drivers
Growing Need to Increase Learning Between Devices and Organization Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices Restraints
Lack of Skilled Technical Expertise Opportunities
Potential to Enable Companies to Leverage a Shared Ml Model Collaboratively by Keeping Data on Devices Capability to Enable Predictive Features on Smart Devices Without Impacting User Experience and Leaking Challenges
Issues of High Latency and Communication Inefficiency System Heterogeneity and Issue in Interoperability Indirect Information Leakage Companies Mentioned
Cloudera Consilient DataFleets Decentralized Machine Learning Edge Delta Enveil Extreme Vision Google IBM Intellegens Lifebit Microsoft NVIDIA Owkin Secure AI Labs Sherpa.ai WeBank For more information about this report visit https://www.researchandmarkets.com/r/9tvzey