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Federated Learning Market Analysis And Trends By Segmentations, Top Key Players, Geographical Expansion, Future Development & Forecast – 2028

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Federated Learning Market Analysis And Trends By Segmentations, Top Key Players, Geographical Expansion, Future Development & Forecast – 2028

October 16
20:50 2023
Federated Learning Market Analysis And Trends By Segmentations, Top Key Players, Geographical Expansion, Future Development & Forecast - 2028
NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK) Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), and Sherpa.AI(Spain).
Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region – Global Forecast to 2028

As per AS-IS scenario, the global federated learning market size to grow from USD 127 million in 2023 to USD 210 million by 2028, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period. Federated learning has started to witness implementations across several verticals with data privacy concerns. The increasing number of websites and mobile applications, the growing high-end customer services, and the rising use of social media platforms, along with the need to acquire customers and to enhance customer services and loyalty, would drive the growth of federated learning market.

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As per AS-IS scenario, among verticals, the Healthcare and Life Sciences segment to to hold the highest market size during the forecast period

The federated learning market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, IT and telecommunications, automotive and transportation and other verticals (media and entertainment, and government). As per AS-IS scenario, the healthcare and life sciences vertical is expected to account for the largest market size during the forecast period. The healthcare and life sciences vertical is under constant pressure to enhance the services it delivers to patients. Unstructured data in the healthcare vertical is growing exponentially. The access to unstructured data, such as output from medical devices, image reports, and lab reports, is not useful to improve patient health. The healthcare and life sciences vertical also include pharmaceutical companies. This vertical is a leader in adopting federated learning solutions with multiple research projects, consortiums, and several deployments.

Europe to hold the largest market size during the forecast period

As per AS-IS scenario, Europe, followed by North America, is estimated to account for the largest market size in the federated learning market during the forecast period respectively. Europe can be considered as one of the early adopters of advanced technologies due to the technical expertise of enterprises in the region. Though businesses in the region witness slow adoption trends, they are focusing on digital transformation, particularly with the integration of new technologies, such as IoT, ML, and AI. Countries in Europe such as the UK, Germany, and France are focusing on developments in federated learning. Countries. In North America, such as the US and Canada, have boosted their investments in sophisticated technologies including AI, IoT, and ML to acquire vast volumes of data from outside organizations, contributing to the region’s market growth. Various verticals in North America are interested in investing in federated learning solutions, including healthcare and life sciences, retail, and eCommerce, and BFSI. Federated learning was born at the convergence of AI, blockchain, edge computing, and IoT. This is expected to drive the growth of federated learning solutions in the region.

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Federated learning allows ML algorithms to gain experience from a broad range of data sets located at different locations. The federated learning approach enables multiple organizations to collaborate for the development of models without the need to directly share secure data with each other. The educated patterns are integrated and transferred to a centralized computer. The data is not shared, and this is how federated learning helps ensure privacy and minimizes communication costs. Federated learning is a new technology approach, which is in the pre-commercial stage, and companies are resisting to discuss their views, experiences, and expectations. Verticals are focusing on data security, hyper-personalization, and contextual recommendation, which will be a key in driving app adoption or eCommerce purchases; federated learning is expected to play a key role here.

Unique Features in the Federated Learning Market:

Its focus on data privacy is one of its distinctive qualities. Without the requirement to share raw data, machine learning models are trained via federated learning across a network of distributed computers or servers. The confidentiality of sensitive information is preserved by only exchanging model updates, or gradients. Industries like healthcare and banking, where data privacy is crucial, are particularly drawn to this novel method.

Another noteworthy quality is efficiency. Federated learning minimises the need for substantial data transfer over networks, making it extremely helpful in situations with constrained or pricey network capacity. Mobile and Internet of Things (IoT) devices, which frequently operate in resource-constrained contexts, are particularly important to this.


Collaborative model training is supported by federated learning as well. Multiple individuals can enhance the model without disclosing their personal information. This makes it perfect for scenarios in which numerous organisations or entities must collaborate in order to improve a shared model.

Additional advantages of federated learning are its reliability and security. The methodology is more resilient to disturbances since it is decentralised; training can still take place even if certain devices or servers go down. Additionally, the method strengthens security against adversarial assaults, making it more dependable than conventional centralised solutions.


Federated learning enables personalization and customisation without centralising user data. This is vital for applications like recommendation systems because it enables the consideration of user preferences without jeopardising personal privacy.


Major Highlights of the Federated Learning Market:


Federated Learning’s capability to improve data privacy stands out as its most notable feature. The pooling and sharing of data across servers in conventional centralised machine learning models raises questions about the security and privacy of the raw data. Federated Learning, on the other hand, avoids this problem by training models on distributed devices and exchanging just model updates or gradients. As a result, businesses with rigorous data privacy laws, like healthcare and banking, find it to be an appealing solution.


Another important feature of federated learning is decentralisation. Instead of being kept in one location, data is dispersed over a network of computers or servers. This strengthens data security and is in line with data protection regulations like the GDPR, which place a strong emphasis on reducing data transport and storage.


It is appropriate for situations where network bandwidth is constrained or expensive due to its notable efficiency in low-bandwidth conditions. Federated learning reduces the need for significant data transfer over networks, making it especially ideal for situations with limited resources, such as the Internet of Things (IoT) and mobile devices.


Highlights include a collaborative model training technique. Multiple parties can work together to improve a common model via federated learning without disclosing their private data. When multiple organisations or entities must work together to improve a model, this collaborative capability is very useful for promoting innovation and teamwork.


And finally, Federated Learning is built to abide by strict data privacy laws. It addresses the rising importance that industries place on these elements by ensuring the highest standards of data privacy and legal compliance.


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Top Key Companies in the Federated Learning Market:

Some of the key players operating in the federated learning market include NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK), Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), and Sherpa.AI(Spain). These federated learning vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global federated learning market.

NVIDIA is specialized in designing Graphics Processing Units (GPUs) for the gaming and professional markets, as well as systems on chip (SOC) units for the mobile computing and automotive market. NVIDIA is one of the leading computing platform company, which is involved in innovating at the intersection of graphics, HPC, and AI. The company is well versed in the manufacturing of graphics-processor technologies for workstations, desktop computers, and mobile devices. The company supplies integrated circuits used for personal-computer GPUs, motherboard chipsets, and game-consoles. NVIDIA is one of the leading player in the federated learning market and it has been involved in developing federated learning modles to train a neural network for brain tumor segmentation. The technique allows data-sharing between hospitals and researchers while preserving patient’s data. The company also has a platform strategy that brings together hardware, software, algorithms, libraries, systems, and services to create unique value for the markets. The company has invested more than USD 24 billion in Research and Development (R&D).

Google, is subsidiary of Alphabet. The company was founded in 1998 and it is headquartered in California, US. The company is specialized in offering platforms such as Android, Chrome, Gmail, Google Drive, Google Maps, Google Play, Search, and YouTube, and each of them has over one billion active users every month.. Google introduced the concept of federated residual learning for models trained from user interaction with mobile devices and to build the most secure and robust cloud infrastructures for processing data. Googles Android 11 uses federated learning to generate smart replies and suggest emojis. Google has built a platform in the cloud and invests in infrastructure, security, data management, analytics, and AI. The company offers its services to clients from verticals, such as automotive, BFSI, retail and eCommerce, education, energy, engineering, entertainment, environment, food and beverage, government, healthcare, manufacturing, media, telecommunications and IT, transportation, and travel and hospitality. As more digital experiences are being built in the cloud, the company’s cloud products are focusing on helping enterprises of all sizes to take advantage of the latest technologies and efficiently run their businesses. Google invests in platforms, such as Android mobile OS, Chrome browser, Chrome OS, and Daydream Virtual Reality (VR). It operates through 70 offices in more than 50 countries worldwide.

Microsoft is specialized in offering software, services, and solutions to compete in the area of an intelligent cloud and edge. With the increasing investments in the mix-reality cloud, Microsoft enables its customers to digitalize their business processes. The company’s offers cloud-based solutions that provide customers with software, platforms, and content. Its product offerings include OS, business solution applications, cross-device productivity applications, software development tools, server applications, desktop and server management tools, and video games. Microsoft’s platforms and tools help to drive the productivity of small and medium businesses, competitiveness of large businesses, and efficiency of the public sector. The company works on three pillars, which include privacy, cybersecurity, and responsible AI, to provide tools and frameworks for its customers to encourage policy change. Microsoft operates in three segments that include productivity and business processes, intelligent cloud, and more personal computing. The company caters to a wide range of verticals, including finance and insurance, manufacturing and retail, energy and utilities, media and entertainment, public sector, healthcare, and IT and telecommunications. It has a presence in more than 190 countries across North America, APAC, Latin America, MEA, and Europe. The Microsoft Research team in March 2020 introduced the concept of federated residual learning. Moreover, in October 2020, Microsoft also introduced a federated approach in training acoustic models.

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