The global federated learning market size was estimated at USD 119.4 million in 2022 and is expected to grow at a compound annual growth rate of 12.7% from 2023 to 2030. Continuous innovation in machine learning (ML) techniques and algorithms significantly enhances the effectiveness of federated learning, making it more appealing for various uses. As AI continues to evolve, federated learning adjusts by adopting these advanced techniques, improving its efficiency in training models across distributed devices. This ongoing progress makes federated learning more attractive, allowing it to serve different industries, such as healthcare and finance, while maintaining strong data privacy across decentralized networks.
Federated learning's scalability across diverse devices is a pivotal driver in attracting a broader user base, enabling businesses of all sizes to access its benefits without substantial infrastructure investments. This accessibility fosters wider adoption across multiple industries seeking innovative AI solutions. Simultaneously, its cost-effectiveness acts as a powerful incentive, lowering entry barriers and enticing a broader spectrum of businesses to adopt this technology. This dual appeal of scalability and cost-efficiency synergistically propels market expansion, consolidating federated learning as a versatile and sought-after solution across diverse sectors, including healthcare, finance, IoT, and more.
Federated learning enables healthcare institutions to train AI models without sharing raw patient data. This preserves sensitive information within each institution while collectively improving diagnostic or treatment models. By pooling knowledge from various healthcare facilities, federated learning enables the creation of more robust and accurate diagnostic or predictive models. For instance, institutions, such as the Mayo Clinic, Cleveland Clinic, and Johns Hopkins Medicine in the U.S., have embraced federated learning. These healthcare organizations utilize federated learning techniques to enhance diagnostic models while preserving patient privacy and confidentiality across their respective networks of hospitals and research facilities.
The IT & telecommunications segment held a dominant market share of over 27.3% in 2022. The IT & telecommunications industry possesses vast and diverse datasets dispersed across various systems and networks. Federated learning aligns with their distributed nature, enabling collaborative model training without compromising sensitive data. The sector’s emphasis on data privacy and security dovetails perfectly with federated learning’s decentralized approach. Moreover, the constant need for innovation and optimization within IT and telecom necessitates efficient utilization of data without centralizing it, a demand met effectively by federated learning. The need for real-time data analysis and processing in IT & telecommunications is met by federated learning's ability to perform on-device training, minimize latency, and enhance network performance.
The healthcare & life sciences segment is expected to register a CAGR of 14.3% over the forecast period. The personalized nature of healthcare often requires customized treatments based on individual patient data. Federated learning enables the creation of more accurate and personalized AI models, enabling advancements in precision medicine without compromising patient confidentiality. Federated learning optimizes the utilization of diverse and extensive datasets available across healthcare institutions. It enables the collective analysis of this data while preserving data privacy, leading to enhanced disease prediction, treatment optimization, and overall healthcare innovation.
North America held the highest market share of over 34.0% in 2022. Key industries, such as healthcare, finance, and technology in North America are early adopters of advanced AI technologies. Federated learning's ability to address data privacy concerns while enabling collaborative model training resonates well with these sectors, leading to widespread adoption and market dominance in the region. The region fosters strong collaborative networks among academia, research institutions, and industries. This collaboration encourages the sharing of expertise, resources, and data, ideal for federated learning's collaborative model training without compromising data privacy.
Asia Pacific is anticipated to witness a CAGR of 14.2% from 2023 to 2030. Countries, such as China, Japan, South Korea, and Singapore, are witnessing significant advancements in AI technologies. These nations are investing heavily in R&D, fostering a thriving ecosystem for AI innovation, including federated learning. Industries in the Asia Pacific are increasingly recognizing the potential of AI solutions for various applications. Federated learning's ability to address data privacy concerns while enabling collaboration resonates with sectors, such as healthcare, finance, and automotive, in this region.
The Industrial Internet of Things (IIOT) segment dominated the market with a revenue share of 24.3% in 2022. The demand growth for federated learning is propelled by its natural alignment with the decentralized structure of IIoT environments. Federated learning’s capacity to train models across distributed devices without centralizing data strongly resonates with the inherently decentralized nature of IIoT. This compatibility fosters adoption within industries reliant on IIoT, driving the expansion of the market. Moreover, its continual enhancement of AI models across various devices within IIoT environments, optimizing operations, serves as a driving force for broader implementation and market growth.
The drug discovery segment is expected to register a significant CAGR over the forecast period. Federated learning’s ability to help different groups collaborate on model training without sharing sensitive information is a big reason why it is growing in the market. By allowing various organizations to work together on drug development without sharing private data, it speeds up the process. This approach gains trust among pharmaceutical companies, research labs, and healthcare groups that want secure ways to work together efficiently. As federated learning proves it can speed up analysis while keeping data safe, more industries are becoming interested in using it, which is driving industry growth.
The large enterprises segment dominated the market with a revenue share of 61.9% in 2022. Large enterprises are increasingly gravitating toward federated learning due to its adaptability to their distributed structure and scale. This approach enables diverse branches or units within these organizations to collaborate on AI model training without centralizing sensitive data, ensuring compliance with stringent privacy regulations. Federated learning accommodates the vast and diverse datasets characteristic of large enterprises, optimizing resource allocation and accelerating model training across different divisions. Its decentralized data handling minimizes the risk of data breaches, aligning with the risk management strategies of these enterprises and fostering a culture of compliance.
The facilitation of collaborative AI model training by federated learning, even for SMEs with limited computational resources, is a key factor propelling market growth. This inclusive approach empowers smaller businesses to collectively refine models using diverse data sources without hefty infrastructure requirements. By enabling SMEs to participate in advanced AI model training without the need for substantial investments, federated learning democratizes access to cutting-edge technology, fostering broader adoption within SMEs. This democratization and resource-efficient nature of federated learning fuel its expansion, driving forward the market for AI solutions among smaller enterprises.
Prominent firms have used product launches and developments, followed by expansions, mergers and acquisitions, contracts, agreements, partnerships, and collaborations, as their primary business strategy to increase their market share. The companies have used various techniques to enhance market penetration and boost their position in the competitive industry. For instance, in March 2023, Consilient, a U.S.-based software company, introduced a solution using federated learning for spotting financial crimes. This special tool helps banks and financial institutions find risky activities by sharing helpful information across different places where data is stored.
Market size value in 2023
USD 128.5 million
Revenue forecast in 2030
USD 297.5 million
CAGR of 12.7% from 2023 to 2030
Base year for estimation
2017 - 2021
2023 - 2030
Market revenue in USD million and CAGR from 2023 to 2030
Revenue forecast, company ranking, competitive landscape, growth factors, and trends
Application, organization size, industry vertical, region
North America; Europe; Asia Pacific; Latin America; MEA
U.S.; Canada; UK; Germany; France; China; Japan; India; South Korea; Australia; Brazil; Mexico; KSA; UAE; South Africa
Key companies profiled
Acuratio Inc.; Cloudera Inc.; Edge Delta; Enveil; FedML; Google LLC; IBM Corp.; Intel Corp.; Lifebit; NVIDIA Corp.
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This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the federated learning market report based on application, organization size, industry vertical, and region:
Application Outlook (Revenue, USD Million, 2017 - 2030)
Industrial Internet of Things
Augmented & Virtual Reality
Data Privacy Management
Organization Size Outlook (Revenue, USD Million, 2017 - 2030)
Industry Vertical Outlook (Revenue, USD Million, 2017 - 2030)
It & Telecommunications
Healthcare & Life Sciences
Retail & E-commerce
Regional Outlook (Revenue, USD Million, 2017 - 2030)
Middle East and Africa
Kingdom of Saudi Arabia (KSA)
b. The global federated learning market size was estimated at USD 119.4 million in 2022 and is expected to reach USD 128.5 million in 2023
b. The global federated learning market is expected to grow at a compound annual growth rate of 12.7% from 2023 to 2030 to reach USD 297.5 million by 2030.
b. North America dominated the federated learning market with a share of 34.0% in 2022. This is attributable to the region's forefront position in AI and machine learning advancements, cultivating an environment that encourages the integration of advanced technologies such as federated learning
b. Some key players operating in the federated learning market include Acuratio Inc.; Cloudera Inc.; Edge Delta; Enveil; FedML; Google LLC; IBM Corporation; Intel Corporation; Lifebit; and NVIDIA Corporation.
b. Key factors driving market growth include the efficiency and scalability of decentralized data processing, allowing model training across distributed datasets without centralizing sensitive information.
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