Federated Learning Market

Market Definition

The market focuses on decentralized machine learning models that train across multiple devices or servers without transferring raw data. It includes tools, platforms, and frameworks that enable secure model updates, edge-device processing, and cross-silo collaboration. Key applications span healthcare, finance, automotive, and mobile technologies, where data privacy and compliance are critical. 

The process involves localized training, encrypted aggregation, and model synchronization. The report provides a comprehensive analysis of key drivers, emerging trends, and the competitive landscape expected to influence the market over the forecast period.

Federated Learning Market Overview

The global federated learning market size was valued at USD 137.5 million in 2024 and is projected to grow from USD 153.1 million in 2025 to USD 362.7 million by 2032, exhibiting a CAGR of 13.11% during the forecast period. 

Market growth is driven by rising concerns over data privacy and increasing restrictions on centralized data storage. Organizations are shifting to decentralized training to comply with regulatory standards. Growing need for collaborative learning across organizations and support from open-source ecosystems and frameworks are further accelerating adoption.

Major companies operating in the federated learning industry are NVIDIA, Google LLC, Microsoft Corporation, IBM, Cloudera, Inc., Intel Corporation, Owkin, Inc., Intellegens Ltd., Enveil Inc., Lifebit Biotech Ltd., DataFleets Ltd., Secure AI Labs, Apheris GmbH, Acuratio Inc., and FedML Inc.

With the rapid expansion of smartphones, sensors, smart home devices, and industrial IoT systems, massive volumes of data are generated at the edge. Transferring this data to the cloud for model training is often inefficient and insecure. Federated learning allows on-device learning, reducing latency and bandwidth costs. This capability is increasing the adoption of federated systems, contributing to market growth.

• In January 2025, researchers from the Sunway University (Malaysia), Al‑Nahrain University (Iraq), and Universiti Kebangsaan Malaysia introduced a novel UAV-assisted federated learning framework that integrates hybrid LoRa P2P/LoRaWAN communication to enhance environmental monitoring in remote biosphere regions. This system enables unmanned aerial vehicles (UAVs) to collect and process data from distributed IoT sensors without relying on centralized cloud infrastructure, thereby preserving data privacy and reducing latency.

Key Highlights

1. The federated learning industry size was valued at USD 137.5 million in 2024.
2. The market is projected to grow at a CAGR of 13.11% from 2025 to 2032.
3. North America held a market share of 36.52% in 2024, with a valuation of USD 50.2 million.
4. The drug discovery & development segment garnered USD 46.1 million in revenue in 2024.
5. The large enterprises segment is expected to reach USD 205.1 million by 2032.
6. The IT & telecommunications segment secured the largest revenue share of 35.20% in 2024.
7. Asia Pacific is anticipated to grow at a CAGR of 14.53% over the forecast period.

Market Driver

Growing Need for Collaborative Learning Across Organizations

The rising demand for secure and collaborative AI development is driving the adoption of federated learning across industries. Organizations are recognizing the value of developing models jointly without sharing raw data. Federated learning enables decentralized training across multiple stakeholders, improving model performance while preserving data privacy. 

Companies in pharmaceuticals, insurance, and manufacturing are increasingly implementing these frameworks, promoting cross-organization collaboration and boosting the growth of the federated learning market globally.

• In December 2024, Google Cloud and Swift announced a collaborative initiative to combat cross-border payment fraud using federated learning technology. This partnership aims to allow multiple financial institutions to collaboratively train AI models on decentralized data, enhancing fraud detection capabilities while preserving data privacy. The project is supported by technology partners Rhino Health and Capgemini, and plans to roll out with 12 global financial institutions in the first half of 2025.

Market Challenge

Standardization and Interoperability Across Systems

A significant challenge impacting the growth of the federated learning market is the lack of standardization and interoperability across different devices, platforms, and data environments. Variations in data formats, hardware capabilities, and communication protocols make it difficult to implement a unified federated learning framework at scale.

To address this challenge, key players are developing cross-platform SDKs, adopting open-source frameworks such as TensorFlow Federated and PySyft, and investing in industry alliances to create shared technical guidelines. These steps help ensure that models can be trained seamlessly across decentralized networks without compromising performance, efficiency, or compliance.

Market Trend

Support from Open-Source Ecosystems and Frameworks

The availability of open-source tools such as TensorFlow Federated, PySyft, and NVIDIA FLARE is supporting widespread experimentation and deployment of federated learning systems. These frameworks reduce the entry barriers for developers and researchers, enabling rapid prototyping and integration into enterprise environments. The expanding community support and continuous technical improvements are driving the expansion of the federated learning market.

• In April 2025, NVIDIA and the PyTorch team at Meta revealed a major collaboration aimed at enabling federated learning (FL) on mobile devices by integrating NVIDIA FLARE with ExecuTorch. NVIDIA FLARE is an open-source, domain-independent SDK designed to be flexible and extensible, allowing researchers and data scientists to transition existing machine learning and deep learning workflows into a federated learning framework.

Federated Learning Market Report Snapshot

Segmentation	Details
By Application	Drug Discovery & Development, Data Privacy Management, Risk Management, Augmented & Virtual Reality, Others
By Organization Size	Large Enterprises
By Industry Vertical	Healthcare & Life Sciences, IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), Retail & E-commerce, Others
By Region	North America: U.S., Canada, Mexico
	Europe: France, UK, Spain, Germany, Italy, Russia, Rest of Europe
	Asia-Pacific: China, Japan, India, Australia, ASEAN, South Korea, Rest of Asia-Pacific
	Middle East & Africa: Turkey, U.A.E., Saudi Arabia, South Africa, Rest of Middle East & Africa
	South America: Brazil, Argentina, Rest of South America

Market Segmentation

• By Application (Drug Discovery & Development, Data Privacy Management, Risk Management, Augmented & Virtual Reality, and Others): The drug discovery & development segment earned USD 46.1 million in 2024, mainly due to its high demand for privacy-preserving collaboration across research institutions.
• By Organization Size (Large Enterprises and SMEs): The large enterprises segment held a share of 58.21% in 2024, fueled by their extensive data assets, strong need for secure collaboration across multiple departments, and substantial resources to invest in advanced AI technologies.
• By Industry Vertical (Healthcare & Life Sciences, IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), Retail & E-commerce, and Others): The IT & Telecommunications segment is projected to reach USD 132.1 million by 2032, owing to its extensive data generation, strong need for privacy-preserving collaborative learning, and rapid adoption of advanced AI technologies to enhance network optimization and customer services.

Federated Learning Market Regional Analysis

Based on region, the market has been classified into North America, Europe, Asia Pacific, Middle East & Africa, and South America.

The North America federated learning market share stood at around 36.52% in 2024, vallued at USD 50.2 million. In North America, strict data protection frameworks such as the California Consumer Privacy Act (CCPA) and evolving federal privacy proposals have intensified the need for privacy-preserving technologies. Enterprises are adopting federated learning to comply with these laws while continuing to leverage data for AI applications. 

Moreover, North America's advanced edge computing ecosystem, including 5G networks, smart city projects, and connected industrial systems, generate large volumes of decentralized data. Federated learning is increasingly being used to process this data locally, enabling faster decision-making and fostering regional market expansion.

• In January 2025, the U.S. National Institute of Standards and Technology (NIST) and the UK's Responsible Technology Adoption Unit concluded a collaborative series on privacy-preserving federated learning. The partnership explored the application of federated learning in cross-border research on rare pediatric cancers, enabling researchers to analyze data across national disease registries without transferring sensitive information.

The Asia-Pacific federated learning industry is expected to grow at a robust CAGR of 14.53% over the forecast period. Increasing data localization regulations and restrictions on cross-border data transfers across the region poses challenges for multinational corporations. Federated learning offers an effective solution by allowing AI models to be trained on locally without transferring data across borders.

• In September 2024, the Hong Kong Applied Science and Technology Research Institute (ASTRI) collaborated with a licensed bank and a logistics platform to employ federated learning technology. This initiative aims to enhance financial inclusion for SMEs by streamlining financing and credit application processes, enabling more efficient access to financial products and services.

Furthermore, digital health startups and hospital networks across Asia Pacific are leveraging federated learning to build collaborative AI models across facilities while keeping patient data secure. The healthcare sector’s need for private, distributed AI solutions is boosting regional market growth.

Regulatory Frameworks

• The U.S. follows a sectoral approach to data privacy. Key laws include the Health Insurance Portability and Accountability Act (HIPAA) for health data protection, the Children’s Online Privacy Protection Act (COPPA) for children's data, and the California Consumer Privacy Act (CCPA) granting consumer data rights in California. These regulations  influence the training of federated learning models involving sensitive, location-based, or regulated data across industries.
• The European Union applies the General Data Protection Regulation (GDPR), which mandates transparency, purpose limitation, and consent in personal data processing. The proposed Artificial Intelligence Act introduces classification-based obligations for AI models. Federated learning,  supports GDPR principles by training models locally and limiting data transfers. However, developers must  must prevent sensitive information leakage through model updates and conduct Data Protection Impact Assessments where required.
• In China, data is regulated through the Personal Information Protection Law (PIPL), Data Security Law (DSL), and Cybersecurity Law. These laws require localization of certain data and explicit user consent for processing. Federated learning supports compliance by enabling AI model training without cross-border data transfer. Nonetheless, systems must adhere to sector-specific standards, particularly in healthcare, finance, and mobility, and ensure that algorithmic outputs do not violate national security laws.

Competitive Landscape

Major players in the federated learning industry are adopting strategies such as expanding privacy-focused service portfolios, investing in secure AI infrastructure, and enhancing support for federated learning technologies. They are also focusing on research and development to improve secure model training and are entering strategic partnerships to strengthen compliance capabilities. 

These efforts reflect the growing priority for data protection and the need to enable collaborative AI without compromising sensitive information, aligning with evolving regulatory requirements.

• In May 2025, Intel expanded its Tiber Trust Services portfolio to bolster data privacy and compliance. The enhancements focus on applying federated learning techniques to train AI models without exchanging private or sensitive data, ensuring data security and adherence to regulatory standards.

List of Key Companies in Federated Learning Market:

• NVIDIA
• Google LLC
• Microsoft Corporation
• IBM
• Cloudera, Inc.
• Intel Corporation
• Owkin, Inc.
• Intellegens Ltd.
• Enveil Inc.
• Lifebit Biotech Ltd.
• DataFleets Ltd.
• Secure AI Labs
• Apheris GmbH
• Acuratio Inc.
• FedML Inc.

Recent Developments (Product Launches)

• In May 2025, Enveil advanced its AI security offerings by supporting encrypted federated learning through its ZeroReveal solutions. This development enables enterprises to perform machine learning on encrypted data without exposing sensitive information, enhancing privacy in AI/ML workflows.
• In March 2025, NVIDIA enhanced its open-source federated learning framework, NVIDIA FLARE, by integrating it with the Flower platform. This integration aims to streamline the development and deployment of federated learning applications across various industries, including healthcare, finance, and manufacturing.
• In January 2025, Google introduced Parfait, a suite of research tools for private AI development. Leveraging its expertise in federated learning, Parfait facilitates training machine learning models across decentralized data sources while preserving user privacy.