Yahoo Finance Based on MarketsandMarkets™, The Federated Learning Market is Projected to Reach $210 Million by 2028, Growing at a CAGR of 10.6% during the Forecast Period
Chicago, Jan. 25, 2023 (GLOBE NEWSWIRE) -- The 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% during the forecast period, according to a new report by MarketsandMarkets™. Primary components such as the enabling businesses to cooperatively utilizing a shared ML model by saving information on devices and the potential to enable predictive features on smart devices without impeding customer experience or leaking personal data are expected to offer growth opportunities for Federated Learning Market.
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143 - Tables
52 - Figures
172 - Pages
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Scope of the Report
Report Metrics | Details |
Market Size value for 2023 | US $127 million |
Market Size value for 2028 | US $210 million |
CAGR Growth Rate | 10.6% |
Largest Market | Europe |
Market size available for years | 2023–2028 |
Base year considered | 2023 |
Forecast period | 2023–2028 |
Segments covered | Application, Vertical and Region |
Geographies covered | North America, Europe, APAC, MEA, and Latin America |
Companies covered | 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), Sherpa.AI(Spain), Decentralized Machine Learning(Singapore), Consilient(US), Apheris(Germany), Acuratio(US), FEDML(US). |
Prototype creation and edge computation, which is centered on federated learning and protected with encryption algorithm, are inclined to make substantial development in the coming years. As one billion-plus smartphones equipped with AI chips and possessing significant computing power get into the market in the next 3–5 years, many of the ML models will be able to run locally on these mobile devices. Distributing the heavy-duty analytics and computations over smartphones “on edge,” as opposed to central computing facilities, will drastically reduce time to develop data products such as hyper-personalized recommendation engines, eCommerce pricing engines. Enterprises will embrace a distributed ML model building framework for taking advantage of faster model deployment and to provide quicker response to fast-changing consumer behavior, besides a vastly reduced cost.
The Federated Learning Market, by verticals is segmented into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, manufacturing, IT and telecommunication, and automotive and transportation verticals. The other verticals such as media and entertainment, and government do not have significant advances at the moment, but they are expected to continue to increase 8 to 10 years as the market continues to innovate. Presently, both healthcare and lifesciences verticals, and BFSI, have the most innovations in the Federated Learning Market, including research studies, confederations, cooperation, and a majority of implementations. These industries are likely to be among the first to implement federated learning solutions. Only a small percentage of firms are projected to examine federated learning solutions in the next 4-5 years, but that number is expected to expand with more discoveries by 2028.
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The Federated Learning Market is segmented to various applications. The applications of federated learning are yet in the exploration stage. Key applications witnessing developments and initial deployments include drug discovery, data privacy and security management, and risk management. Federated learning has the ability to retrieve the greatest quantity of knowledge from datasets, allowing for incremental virtualization of drug development operations and increased efficiency in getting ever more better drug targets into clinical studies.
Federated Learning Market in terms of geographic presence is segmented into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America (LA). Europe, followed by North America, is estimated to account for the largest market size in the Federated Learning Market during the forecast period respectively. The elements predicted to accelerate progress includes stringent data restrictions with the heavy emphasis upon information security, the concentration on innovation through studies, and rapid technological architecture advances throughout sectors. The Federated Learning Market in APAC is projected to grow at the highest Compound Annual Growth Rate (CAGR) from 2023 to 2028. The implementation of technological innovations such as big data analytics, AI, and IoT, and also continued attempts to incorporate data regulatory requirements, as well as a concentrate on hyper customization and interactional suggestion in assistance of the town's rapidly growing online market through key nations such as China, India, and Japan, are anticipated to propel the growth of federated learning solutions.
Major vendors in the global 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).
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