Federated Learning Market Size, Share, Opportunities, And Trends By Component (Hardware, Software), By Enterprise Size (Large Enterprises, Small And Medium Enterprises), By End-User (Banking, Financial Services, And Insurance (BFSI), Retail And E-Commerce, Healthcare, IT & Telecommunication, Automotive, Others), And By Geography – Forecasts From 2025 To 2030
- Published : Jul 2025
- Report Code : KSI061617579
- Pages : 140
Federated Learning Market Size:
The federated learning market is anticipated to expand at a high CAGR over the forecast period.
The federated learning market is growing as it allows AI models to be trained on several dispersed servers or devices without exchanging raw data. Federated learning sends the model to local devices where training takes place, rather than transferring data to a central server. This method greatly improves data security and privacy. It is perfect for sectors involving sensitive data, such as healthcare, finance, and telecommunications.
Additionally, it lowers bandwidth consumption and latency. This makes it helpful in edge computing and Internet of Things settings. Federated learning provides a privacy-preserving substitute for conventional AI techniques. This is because global data regulations such as GDPR and HIPAA have become more stringent. Its adoption is being fuelled by its capacity to provide real-time, personalised insights without jeopardising user data.
Federated Learning Market Overview & Scope:
The federated learning market is segmented by:
- Component: Software holds a significant share of the federated learning market. This is because they have the foundation for building, deploying, and managing federated learning systems. Privacy-preserving algorithms and data encryption tools need different software to operate. Leading companies and research institutions are developing specialised software solutions that support decentralised model training.
- Deployment: Large enterprises hold a substantial share of the federated learning market. This is because they have an extensive data system. These organisations operate across multiple departments, regions, or subsidiaries. They need to maintain strict regulatory compliance requirements. Federated learning helps them to train AI models without breaking any data protection laws.
- End User: Retail and e-commerce hold a considerable share of the federated learning market. This is because they deliver personalised experiences to their customers. They need to be compliant with customer data protection laws. They collect data from various sources, like websites and mobile apps. Federated learning allows companies to train AI models on this data locally. This helps them to be compliant with laws like GDPR and CCPA.
- Region: The Asia-Pacific federated learning market is experiencing steady growth. This is due to the rising concerns over data privacy and the rapid digitalisation of various sectors. Countries like China and India have become early adopters of federated learning. They have heavily invested in AI research and edge computing devices.
Top Trends Shaping the Federated Learning Market:
- Rising Adoption in Healthcare and Finance: A trend in the federated learning market is the rising adoption of federated learning in healthcare and finance. These industries handle highly sensitive data, and federated learning helps in ensuring data privacy. It allows multiple collaborations between these industries without sharing any personal records.
- Advancements in Privacy: Preserving Technologies- Another significant trend is the advancements in privacy-preserving technologies. These technologies have features like differential privacy, secure multi-party computation, and homomorphic encryption.
- Emergence of Federated Learning-as-a-Service: There has been an increase in the emergence of federated learning-as-a-service.These services provide prebuilt frameworks and APIs. They help to deploy federated learning without technical expertise. It also reduces the development and increases the adoption. These services will streamline the use of federated learning. They will be used among small enterprises and non-tech firms.
Federated Learning Market Growth Drivers vs. Challenges:
Drivers:
- Growing Demand for Data Privacy and Security: One of the key drivers of the federated learning market is the rise in demand for data privacy and security. Cyberthreats and misuse of personal information have largely increased over the past few years. Stringent data protection laws such as GDPR, HIPAA, and CCPA have forced companies to ensure data confidentiality and regulatory compliance. According to the World Economic Forum’s report on “Global Cybersecurity Outlook 2025”, the cybersecurity insurance market is expected to grow from $14 billion in 2023 to $29 billion by 2027.
- Rapid Growth of Edge Devices and IoT Networks: Another key driver of the federated learning market is the rise in the growth of edge devices and the usage of IOT networks. This has led to an increase in data generated from smartphones and other devices. Federated learning helps AI models to be trained directly on these edge devices. This reduces latency and helps in quick decision-making.
Challenges:
- Data Heterogeneity: One of the major challenges of the federated learning market is data heterogeneity. Data remains decentralised across multiple devices in federated learning. Data varies from one source to another in format, distribution, and quality. Data heterogeneity makes it difficult to train a unified global model. These global models are performing consistently across all platforms. For instance, different hospitals use different diagnostic equipment and maintain records in different formats. As a result, the local models trained on each client’s data may differ significantly
Federated Learning Market Regional Analysis:
- North America: The North American federated learning market is experiencing strong growth, due to the increasing concerns over data privacy. Other factors leading to this growth are the increasing adoption of AI across sectors. With the help of federated learning, one can use collaborative machine learning without sharing raw data. Industries such as finance and telecommunications are using federated learning platforms to handle data. The United States has become a leader in the adoption of federated learning with the help of innovation and investment. The growing deployment of edge devices and IoT networks is another factor driving the market.
Federated Learning Market Competitive Landscape:
The market has many notable players, including. NVIDIA Corporation, Cloudera INC., IBM Corporation, Microsoft Corporation, Alphabet Inc., Owkin Inc., Apheris AI GmbH, FedML Inc., Intel Corporation, Sherpa.AI, among others.
- Partnership: In December 2024, Google announced its partnership with Swift. This strategic partnership would help build Swift an anti-fraud technology using AI and federated learning. This collaboration will also show the potential of federated learning and confidential computing
- Collaboration: In August 2024, Axis Bank launched an AI pilot to tackle fraud in collaboration with SWIFT. This collaboration, along with other global banks, focuses on developing the use of secure data collaboration and federated learning technologies to detect fraud.
- Partnership: In April 2024, FEDML announced its partnership with DENSO to provide a generative AI platform, which is FEDML Nexus AI. This platform has features for wide application, including federated features with secure federated learning.
Federated Learning Market Segmentation:
By Component
- Hardware
- Software
By Enterprise Size
By End-User
- Banking, Financial Services, and Insurance (BFSI)
- Retail and E-Commerce
- Healthcare
- IT & Telecommunication
- Automotive
- Others
By Region
1. EXECUTIVE SUMMARY
2. MARKET SNAPSHOT
2.1. Market Overview
2.2. Market Definition
2.3. Scope of the Study
2.4. Market Segmentation
3. BUSINESS LANDSCAPE
3.1. Market Drivers
3.2. Market Restraints
3.3. Market Opportunities
3.4. Porter’s Five Forces Analysis
3.5. Industry Value Chain Analysis
3.6. Policies and Regulations
3.7. Strategic Recommendations
4. TECHNOLOGICAL OUTLOOK
5. Federated Learning Market by Component
5.1. Introduction
5.2. Hardware
5.3. Software
6. Federated Learning Market BY Enterprise Size
6.1. Introduction
6.2. Large Enterprises
6.3. Small and Medium Enterprises
7. Federated Learning Market BY End-User
7.1. Introduction
7.2. Banking, Financial Services, and Insurance (BFSI)
7.3. Retail and E-Commerce
7.4. Healthcare
7.5. IT and Telecommunication
7.6. Automotive
7.7. Others
8. Federated Learning Market BY GEOGRAPHY
8.1. Introduction
8.2. North America
8.2.1. USA
8.2.2. Canada
8.2.3. Mexico
8.3. South America
8.3.1. Brazil
8.3.2. Argentina
8.3.3. Others
8.4. Europe
8.4.1. United Kingdom
8.4.2. Germany
8.4.3. France
8.4.4. Italy
8.4.5. Spain
8.4.6. Others
8.5. Middle East & Africa
8.5.1. Saudi Arabia
8.5.2. UAE
8.5.3. Others
8.6. Asia Pacific
8.6.1. China
8.6.2. India
8.6.3. Japan
8.6.4. South Korea
8.6.5. Thailand
8.6.6. Others
9. COMPETITIVE ENVIRONMENT AND ANALYSIS
9.1. Major Players and Strategy Analysis
9.2. Market Share Analysis
9.3. Mergers, Acquisitions, Agreements, and Collaborations
9.4. Competitive Dashboard
10. COMPANY PROFILES
10.2. Cloudera INC.
10.3. IBM Corporation
10.4. Microsoft Corporation
10.5. Alphabet Inc.
10.6. Owkin Inc.
10.7. Apheris AI GmbH
10.8. FedML, Inc.
10.9. Intel Corporation
10.10. Sherpa.AI
11. APPENDIX
11.1. Currency
11.2. Assumptions
11.3. Base and Forecast Years Timeline
11.4. Key benefits for the stakeholders
11.5. Research Methodology
11.6. Abbreviations
NVIDIA Corporation
Cloudera INC.
IBM Corporation
Microsoft Corporation
Alphabet Inc.
Owkin Inc.
Apheris AI GmbH
FedML, Inc.
Intel Corporation
Sherpa.AI
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