Global Federated Learning Market to Grow at a CAGR of 10.7% During 2024-2032, Aided by the Growing Automation Demand from the Automotive Sector

North America holds a significant market share in the market due to the presence of leading technology companies such as Google LLC, IBM Corporation (NYSE: IBM), NVIDIA Corporation, and others. According to industry reports, the revenue of technology firms in the U.S. has grown by over 5.4% annually.

Industry leaders across different industry verticals employ various technologies to modernise outdated architectures by moving to cloud resources, improving business processes, and increasing the usage of intelligent automation. The growing automation demand from the automotive sector due to the rising popularity of self-driving cars further creates opportunities for the federated learning market expansion. The ML algorithm makes automated vehicles reliable and secure by analysing personal data.

Europe is another significant region adopting such technologies due to the robust expansion of the healthcare sector. There is an escalating demand for federated learning in drug discovery, which further contributes to the market growth.

In July 2023, Robert Bosch GmbH partnered with the Austrian Institute of Technology (AIT) to strengthen their research partnership in the field of federated learning over the next three years. The long-standing cooperation between Bosch Research and AIT in the area of control engineering serves as the foundation for the partnership with the AIT Centre for Vision, Automation, and Control. Algorithms that learn locally and adaptively have been the main focus.

The company has planned to link devices and components that can share knowledge by utilising federated learning. The objective of this research partnership is to apply the notion of federated learning, which was formulated in several IT domains, intricate, dynamic, non-linear systems, incorporate it into specific applications, and capitalise on the benefits of federated learning for Bosch.

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Market Analysis by Deployment Type, Application, End Use, and Region:

• The market, on the basis of deployment type, can be divided into cloud based and on-premises.

• Based on application, the market can be divided into industrial internet of things, visual object detection, drug discovery, risk management, augmented and virtual reality, and data privacy management, among others.

• The different end uses for federated learning include retail and e-commerce, automotive, IT and telecommunication, healthcare, BFSI, and manufacturing, among others.

• The major regional markets for federated learning include North America, Europe, the Asia Pacific, Latin America, and the Middle East and Africa.

Key Findings of the Report:

• In 2022, the manufacturing sector accounted for 16% of the world's GDP. Federated learning plays a pivotal role in the manufacturing sector by offering both intra and inter company applications, where a company with multiple branches trains a model together with one or more other companies.

Table: Growth of Manufacturing Sector (% of World GDP), 2018-22

• The automotive sector significantly contributes to the growth of the global economy. Reportedly, in 2022, 85.02 million motor vehicles were produced around the world, an increase of 5.7% compared to 2021. The growing application of federated learning facilitated by artificial intelligence (AI) in self-driving cars drives the federated learning market expansion.

• The market for federated learning is expected to rise rapidly in the forecast period amid the growing applications of virtual and augmented reality in a variety of industries, including education, gaming, event management, and healthcare, among others.

• Development in AI helps the aerospace sector by improving engineering processes and fuelling efficiency, part design, and inspection processes. The aviation industry is also benefiting from federated learning by allowing increased flexibility and creativity in design, decreased weight and fuel consumption, time and cost savings in production and supply chain, sustainability, and circularity.

Key Offerings of the Report:

• The EMR report gives an overview of the global federated learning market for the periods (2018-2023) and (2024-2032).

• The report also offers the historical (2018-2023) and forecast (2024-2032) markets for the deployment types, applications, end uses, and major regions of federated learning.

• It assesses the regional price trends for the (2018-2023) and (2024-2032) periods.

• The market report also analyses the trade data by covering the major importing and exporting countries.

• The report analyses the market dynamics, covering the key demand and price indicators in the market, along with an assessment of the SWOT and Porter’s Five Forces models.

The major players in the global federated learning market are Google LLC, Intel Corporation, Barron Associates Inc., Sherpa.ai., Apheris AI GmbH, IBM Corporation, Cloudera, Inc., NVIDIA Corporation, Acuratio Inc., and Consilient Inc., among others. The comprehensive report by EMR looks into the market share, capacity, and latest developments like mergers and acquisitions, plant turnarounds, and capacity expansions of the major players.

Key Highlights of the Report

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