Federated Learning Market Research Report- Segmented By Application (Drug Discovery, Shopping Experience Personalization, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, Others); Industry Vertical (IT & Telecommunication, BFSI, Healthcare & Life Sciences, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, Others); and Region - Size, Share, Growth Analysis | Forecast (2023 – 2030)

Federated Learning Market Size (2023 – 2030)

The Global Federated Learning Market valued at USD 114.82 Million and is projected to reach a market size of USD 198 Million by the end of 2030. Over the forecast period of 2024-2030, the market is projected to grow at a CAGR of 10.4%.



Federated learning is a type of machine learning that spreads an algorithm over several decentralised endpoints or servers, each of which has access to a small amount of local data. In contrast to conventional centralised machine learning techniques, this approach keeps all local datasets on a single server. This method also makes sure that local data samples are sent to the server in the same manner. Without disclosing any personal information, federated learning may be used to create consumer behaviour models from the smartphone data pool for applications such as next-word prediction, voice recognition, facial recognition, and other uses. In order to handle important challenges like data access rights, data privacy and security, and the ability to access heterogeneous data, federated learning enables diverse suppliers to design a common machine learning algorithm without sharing data. Businesses that may use federated learning to enhance their operations include those in the defence, telecommunications, and pharmaceutical industries.

The market for federated learning solutions is expanding as a result of the rising need for better data security and privacy as well as the growing necessity to adjust data in real-time to enhance conversions automatically. Additionally, by keeping data on devices, these solutions help businesses use machine learning models, propelling the federated learning industry. Additionally, there is attractive potential for the federated learning industry to grow over the next few years because of the ability to offer predictive capabilities on the newest smart devices without compromising the user experience or disclosing private information.


Practically every business has been influenced by COVID-19, a worldwide public health disaster that has never before occurred. Its long-term effects have had a substantial impact on multiple markets in numerous nations throughout the world. Additionally, governments throughout the world implemented lockdowns to control the spread of the dangerous COVID-19 illness. Due to travel limitations, these lockdowns seriously interfered with the global supply chain for all goods and services. Thousands of workers were forced to labour remotely due to the infection's rapid global spread, which caused an economic standstill. But to foresee and look into the expansion of prospective data alarms in several nations throughout the world, artificial intelligence and machine learning were mostly utilised. By leveraging real-time data, artificial intelligence is crucial to comprehending the forecast of COVID-19's future reachability across nations. Throughout the predicted period, this is anticipated to persist. Consequently, COVID-19 has a favourable effect on the market for federated learning systems.


Increasing the use of federated learning in a variety of applications is likely to propel the market growth:

Federated learning is transforming how ML approaches are taught. Organizations are focusing their efforts on doing in-depth research on federated learning. Employing federated learning, businesses may reinforce their present algorithms and improve their AI applications. More learning is becoming more and more in demand from enterprises and devices alike. In the field of wellness, federated learning might help medical professionals deliver high-quality results and hasten the discovery of new drugs.

Federated learning facilitates group learning among different users:

A technique for training ML algorithms on distributed data is called federated learning. The ML machines are taught on-the-fly, and information is preserved at sources like cellphones, factory detecting equipment, and other end devices rather than being stored on a single computer or data mart. Before transmitting back to a central computer, this aids in decision-making. For instance, federated learning is effective in this banking industry for analysing debt risk. Typically, banks use whitelisting techniques to block customers based on credit card information from the federal reserve. Risk assessment factors like taxation and reputation may be employed by collaborating with other financial institutions and eCommerce businesses. Companies may employ federated learning to develop a risk evaluation ML model because it is risky to share personal consumer data between organisations. Because of such a data transfer, information is exposed to hackers. Utilizing the federated learning approach, ML methods are disseminated. Sharing and storing data from the device, enables enterprises to develop a shared paradigm collaboratively.


The lack of a skilled workforce is a major restraining factor:

When integrating ML into current operations, many businesses have major challenges due to a shortage of competent staff, particularly IT specialists. Being a novel concept, federated learning systems are difficult for personnel to understand and execute. Due to a shortage of qualified individuals to develop and perform federated learning activities that need complex methodologies like machine learning, hiring and maintaining technical talents became a top worry for certain firms. They must develop their organisational capabilities and job titles. To install and maintain machine learning algorithms, for instance, organisations require engineers that can manage and grasp the current federated learning architecture. The best-trained scientists are data scientists, who have a deep knowledge of computer science, statistics, and conceptual comprehension. On the other side, qualified data scientists demand high fees and demand items that are frequently out of the price range of SMEs or even large enterprises. The demand for federated learning modules across industries is rising as a result of the need to remain relevant in a market with limited capabilities. The current lack of skilled workers is a major obstacle to the global market for federated learning solutions as a result.

The issue regarding System integration and interoperability may hamper the market growth:

System diversity is produced by users with different levels of computing and network control. Such variation in personal computer rates challenges the viability of federated algorithms and substantially reduces their theoretical execution. Federated learning maintains information locally, including cell devices or institutions, while building predictive algorithms utilising remote computers and walled storage systems. It was necessary to make a fundamental move away from conventional methodologies to massive ML, distributed optimization, and data processing while retaining privacy since education in heterogeneous and potentially huge networks presented new challenges. Every component of federated systems may have separate storage, processing, and communication network because of variations in energy and internet connection technologies (3G, 4G, 5G, and Wi-Fi). the architecture and internet backbone capacity limitations due to the million-device network. Federated learning has a significant barrier because of the variability in the various hardware requirements and fluctuating settings across participating devices. Theoretically, heterogeneity might significantly affect the training phase of federated learning by, for example, making a unit unreachable for learning or preventing it from submitting model changes.




Market Size Available

2022 - 2030

Base Year


Forecast Period

2023 - 2030



Segments Covered

By Application, Industry Vertical, and Region

Various Analyses Covered

Global, Regional & Country Level Analysis, Segment-Level Analysis, DROC, PESTLE Analysis, Porter’s Five Forces Analysis, Competitive Landscape, Analyst Overview on Investment Opportunities

Regional Scope

North America, Europe, APAC, Latin America, Middle East & Africa

Key Companies Profiled


This research report on Federated Learning Market has been segmented and sub-segmented based on Application, By Industry Vertical and By Region.


  • Drug Discovery

  • Shopping Experience Personalization

  • Risk Management

  • Online Visual Object Detection

  • Data Privacy & Security Management

  • Industrial Internet of Things

  • Augmented Reality/Virtual Reality

  • Others

Based on Application, the market is segmented into Drug Discovery, Shopping Experience Personalization, Risk Management, Online Visual Object Detection, Data Privacy & Security Management, Industrial Internet of Things, Augmented Reality/Virtual Reality, and Others. The federated learning market's revenue was significantly dominated in 2021 by the industrial internet of things segment. In contemporary IoT networks, such as those found in wearable technology, self-driving cars, and smart homes, sensors are used to collect data and act on it instantly. An updated model of traffic, pedestrian, or construction behaviour could be necessary for a fleet of autonomous cars to function successfully. It may be difficult to build aggregate models in these situations because of privacy issues and the limited connection of each device. Training models that can react quickly to changes in these systems while protecting user privacy are made possible by federated learning approaches. The segment's growth is driven by this aspect.


  • IT & Telecommunication

  • BFSI

  • Healthcare & Life Sciences

  • Energy & Utilities

  • Manufacturing

  • Automotive & Transportation

  • Retail & Ecommerce

  • Others

Based on Vertical, the market is segmented into IT & Telecommunication, BFSI, Healthcare & Life Sciences, Energy & Utilities, Manufacturing, Automotive & Transportation, Retail & Ecommerce, and Others. The market for federated learning had its greatest revenue share in 2021 in the healthcare and life sciences category. The fact that the healthcare and life sciences sector is continually under pressure to raise the standard of services it offers to customers is responsible for the segment's rising growth. The volume of unstructured data in the healthcare sector is substantially growing. Access to unstructured data, such as test results, imaging reports, and output from medical devices, is unhelpful in enhancing patient health. The healthcare and life sciences sector also includes pharmaceutical companies. The use of federated learning technologies in the healthcare and life sciences industries is accelerating because of several research programmes, consortiums, and deployments.

During the projected period, the automotive and transportation vertical is anticipated to develop at the greatest CAGR. The technology behind autonomous vehicles is complex. The system uses a variety of technologies, including observation, forecasting, monitoring, localisation, modelling, interfaces utilising public clouds, and data management. In addition to making automated cars dependable and secure for smooth integration throughout the globe, the focus of the launch of automated vehicles was on data, edge-to-edge computer technology management, and better ML algorithm. The rise of federated learning in the sector is anticipated to be fueled by effective learning, which selects the most pertinent data points to categorise and contribute to the instructional pool.


  • North America

  • Europe

  • The Asia Pacific

  • Latin America

  • The Middle East

  • Africa

By region, the Federated Learning Market is grouped into North America, Europe, Asia Pacific, Latin America, The Middle East and Africa. During the projected period, Europe is anticipated to have the greatest market share for federated learning systems. Medical imaging and diagnostics, precision medicine, lifestyle management and monitoring, drug development, inpatient care and hospital administration, virtual assistant, wearables, and research are some of the applications that make up the federated learning market for the healthcare industry. The lengthy process of discovering new drugs necessitates the analysis of enormous amounts of bioscience data, such as patents, genetic data, and the numerous papers that are posted daily across all scientific journals and databases. Due to this, the drug development process must advance, and federated learning has the power to affect and improve this process. Vendors in the industry are therefore creating new items to provide a better platform throughout the market. The problems associated with ageing populations and a lack of healthcare personnel in Europe are accelerating the deployment of AI technology in the healthcare sector. In turn, this is fueling the expansion of the European federated learning market.

The North American region is also likely to play a significant role in market development over the forecast period of 2023 - 2030. The existence of industrialised nations like the United States and Canada. Strict data restrictions, a focus on innovation in research, attention to data protection, and quick technological improvements among end-users are what enable the acceptance and deployment of federated learning systems could be attributed to this growth in the region. The region's market expansion is anticipated to be fueled by the rising usage of emerging technologies like artificial intelligence, machine learning, big data analytics, and the internet of things.


Some of the major players operating in the Federated Learning Market include:






  6. OWKIN, INC.






  • PRODUCT LAUNCH- Nvidia released FLARE, an open-source software platform, in December 2021. Federated Learning Application Runtime Environment, or FLARE, aims to give federated learning a shared computing platform.
  • PRODUCT LAUNCH- Google's Smart Text Selection programme included federated learning in November 2021. With this launch, the business wanted to make the process of training the neural network model across user interactions easier while increasing user privacy. Additionally, new developments would make it possible for the models to be taught on-device using actual interactions by utilising federated learning.
  • PRODUCT LAUNCH- Edge Delta introduced an open demo environment in July 2021. With the new approach, customers may freely explore a fully working environment, the real-time insights being produced, and the worth of the platform's live continuous streaming data without being asked for their payment information or login credentials.
  • PRODUCT LAUNCH- IBM unveiled IBM Federated Learning on Github in July 2020. With this introduction, the business wanted to provide its clients with a framework that would enable them to improve their model training using data gathered from various sources while protecting data privacy.


Chapter 1.Federated Learning Market – Scope & Methodology

1.1. Market Segmentation

1.2. Assumptions

1.3. Research Methodology

1.4. Primary Sources

1.5. Secondary Sources

Chapter 2. Federated Learning Market – Executive Summary

2.1. Market Size & Forecast – (2023 – 2030) ($M/$Bn)

2.2. Key Trends & Insights

2.3. COVID-19 Impact Analysis

       2.3.1. Impact during 2023 - 2030

       2.3.2. Impact on Supply – Demand

Chapter 3. Federated Learning Market – Competition Scenario

3.1. Market Share Analysis

3.2. Product Benchmarking

3.3. Competitive Strategy & Development Scenario

3.4. Competitive Pricing Analysis

3.5. Supplier - Distributor Analysis

Chapter 4. Federated Learning Market Entry Scenario

4.1. Case Studies – Start-up/Thriving Companies

4.2. Regulatory Scenario - By Region

4.3 Customer Analysis

4.4. Porter's Five Force Model

       4.4.1. Bargaining Power of Suppliers

       4.4.2. Bargaining Powers of Customers

       4.4.3. Threat of New Entrants

       4.4.4. Rivalry among Existing Players

       4.4.5. Threat of Substitutes

Chapter 5. Federated Learning Market - Landscape

5.1. Value Chain Analysis – Key Stakeholders Impact Analysis

5.2. Market Drivers

5.3. Market Restraints/Challenges

5.4. Market Opportunities

Chapter 6. Federated Learning Market – By Application

6.1. Drug Discovery

6.2. Shopping Experience Personalization

6.3. Risk Management

6.4. Online Visual Object Detection

6.5. Data Privacy & Security Management

6.6. Industrial Internet of Things

6.7. Augmented Reality/Virtual Reality

6.8 Others

Chapter 7. Federated Learning Market – By Industry Vertical

7.1. IT & Telecommunication

7.2. BFSI

7.3. Healthcare & Life Sciences

7.4. Energy & Utilities

7.5. Manufacturing

7.6. Automotive & Transportation

7.7. Retail & Ecommerce

7.8. Others

Chapter 8. Federated Learning Market - By Region

8.1. North America

8.2. Europe

8.3. Asia-Pacific

8.4. Latin America

8.5. The Middle East

8.6. Africa

Chapter 9. Federated Learning Market-Key Players

9.1    NVIDIA
9.6    OWKIN, INC.
9.10    ENVEIL INC



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