Federated Learning Market

Federated Learning Market worth $210 million by 2028

According to a research report "Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region - Global Forecast to 2028" published by MarketsandMarkets, As per AS-IS scenario, 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. Federated learning is a revolutionary innovation technique that is still in its experimental stage. Currently, healthcare and life sciences, and BFSI verticals have the most developments. Various other verticals are also concentrating on information security, tailoring, and referral, which are all important factors in federated learning solution success.

Browse 143 market data Tables and 52 Figures spread through 172 Pages and in-depth TOC on "Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region - Global Forecast to 2028"
View detailed Table of Content here - https://www.marketsandmarkets.com/Market-Reports/federated-learning-solutions-market-151896843.html

As per AS-IS scenario, among verticals, the Healthcare and Life Sciences segment to to hold the highest market size during the forecast period

The federated learning market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, IT and telecommunications, automotive and transportation and other verticals (media and entertainment, and government). As per AS-IS scenario, the healthcare and life sciences vertical is expected to account for the largest market size during the forecast period. The healthcare and life sciences vertical is under constant pressure to enhance the services it delivers to patients. Unstructured data in the healthcare vertical is growing exponentially. The access to unstructured data, such as output from medical devices, image reports, and lab reports, is not useful to improve patient health. The healthcare and life sciences vertical also include pharmaceutical companies. This vertical is a leader in adopting federated learning solutions with multiple research projects, consortiums, and several deployments.

Europe to hold the largest market size during the forecast period

As per AS-IS scenario, Europe, followed by North America, is estimated to account for the largest market size in the federated learning market during the forecast period respectively. Europe can be considered as one of the early adopters of advanced technologies due to the technical expertise of enterprises in the region. Though businesses in the region witness slow adoption trends, they are focusing on digital transformation, particularly with the integration of new technologies, such as IoT, ML, and AI. Countries in Europe such as the UK, Germany, and France are focusing on developments in federated learning. Countries. In North America, such as the US and Canada, have boosted their investments in sophisticated technologies including AI, IoT, and ML to acquire vast volumes of data from outside organizations, contributing to the region’s market growth. Various verticals in North America are interested in investing in federated learning solutions, including healthcare and life sciences, retail, and eCommerce, and BFSI. Federated learning was born at the convergence of AI, blockchain, edge computing, and IoT. This is expected to drive the growth of federated learning solutions in the region.

Major vendors in the global federated learning solutions 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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Report Code
TC 7866
PR Published ON
4/29/2022
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