Federated Learning Market Size, Share, Industry, Forecast and outlook (2024-2031)

Federated Learning Market is Segmented By Application (Drug Discovery, Shopping Experience Personalization, Data Privacy, and Security Management, Risk Management, Industrial Internet of Things, Online Visual Object Detection, Augmented Reality/Virtual Reality, Others), By End-User (BFSI, Healthcare and Life Sciences, Retail and eCommerce, Manufacturing, Energy and Utilities, Automotive and Transportation, IT and Telecommunication, Others), By Region (North America, Latin America, Europe, Asia Pacific, Middle East, and Africa) – Share, Size, Outlook, and Opportunity Analysis, 2024-2031

Market Overview

The Report on Global Federated Learning Market is estimated to reach at a CAGR of 10.90% during the forecast period 2024-2031. Federated Learning Market analyses the market size, shares, recent trends, competitive intelligence, and future market outlook. 

Federated Learning Market usage in drug discovery is on the rise. Demand from BFSI, healthcare, and life sciences in ‘Europe’ is rising. The competitive rivalry intensifies with NVIDIA, Cloudera, IBM, Microsoft, and others active in the market. The traditional method of training Artificial intelligence models entails setting up servers on which models are trained on data, often using a cloud-based computing platform. However, a new model creation method known as federated learning has emerged in recent years. Federated learning takes machine learning models to the data source rather than the model to the data. Federated learning connects multiple computational devices into a decentralized system that allows individual data collection devices to help train the model. In a federated learning system, each of the numerous devices that are a member of the learning network has a copy of the model on the device. A central device or server receives the parameters/weights from each model, aggregates them, and updates the overall model. Repeat this procedure as necessary to reach the required level of precision. In essence, federated learning works by never transmitting training data across devices or parties.

Market Scope 

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Market Dynamics 

The ability to secure data and prevent private data aggregation on servers is a major market driver for the global federated learning market. Nonetheless, the technical challenges associated with federated learning could be a major market restraint.

Ability to secure data and prevent aggregation of private data on servers

Accurate machine learning models are valuable to businesses and traditional centralized machine learning approaches have flaws such as a lack of continuous learning on edge devices and the aggregation of private data on central servers. Federated learning alleviates these issues. A central ML model is built in a centralized environment using all available training data in traditional machine learning. When a central server can serve the predictions, this works flawlessly.

However, in mobile computing, users expect immediate responses and the communication time between a user device and a central server may be too slow to provide a satisfactory user experience. To overcome the aforementioned predicament, the model can be placed in the end-user device, but then continuous learning becomes difficult because models are trained on a complete data set to which the end-user device does not have access. Another issue with traditional machine learning is that user data is aggregated in a centralized location for machine learning training, which may violate certain countries' privacy policies and make the data more vulnerable to data breaches. Federated learning overcomes these challenges by enabling continuous learning on end-user devices while ensuring that end-user data is not lost. As a result, the ability of federated learning to protect private data without aggregating it in a central server is a major driver for the global federated learning market.

Technical challenges associated with federated learning

Because federated learning is still in its early stages, several challenges must be overcome before reaching its full potential. The potential bottlenecks for federated learning approaches are edge device training capabilities, data labeling, standardization, and model convergence. When designing federated learning approaches, the computational capabilities of edge devices for local training must be considered. While most smartphones, tablets, and other IoT-compatible devices can train machine learning models, this typically degrades the device's performance. Model accuracy and device performance will have to be sacrificed.

Data uniformity and labeling are other issues that federated learning systems must solve. Clear and consistent training data labeling is needed for supervised learning models, but it can be challenging to accomplish across the system's numerous client devices. The development of model data pipelines that automatically consistently apply labels based on events and user activities is crucial. Model convergence time, which often takes federated learning models longer to converge than locally trained models, is another difficulty for federated learning. Since network problems, strange updates, and even different application use periods can contribute to longer consolidation times and lower reliability, more devices used in training will make the model more unpredictable.

As a result, federated learning solutions are typically most useful when they offer significant advantages over centrally training a model, such as when datasets are extremely large and dispersed. Further, since such technical challenges limit the application of federated learning, the respected factor could be recognized as a major market restraint.

COVID-19 Impact on Market

Despite COVID-19 putting humanity in an unprecedented and uncertain situation, it also has provided mankind with some certainty regarding future demands, particularly in the healthcare sector. Because of the COVID-19 crisis, humanity brought significant virtualization-based solutions to modernity. Federated learning and AI have quickly emerged as one of the best defenses an institution can have to ensure business and institutional continuity in this situation.

For instance, NVIDIA FLARE, Federated Learning Application Runtime Environment, was launched in November 2021. It is an open-source platform based on the foundation of NVIDIA Clara Train's federated learning software and was used for biomedical imagery, functional genomics, cancer, and COVID-19 research. Researchers and data scientists could use this SDK to convert their current ML techniques processes to a decentralized network. NVIDIA FLARE supports several networked topologies, including peer-to-peer, asynchronous, and server-client techniques.

The demand for similar technologies is expected to grow even further as the implementation of federated learning in COVID-19 studies and healthcare has already proved extremely useful. As a result, the market would experience further traction.

Market Segmentation Analysis

The global federated learning market is classified based on application, end-user, and region.

The need to advance the healthcare sector to address the growing medical complexities and efficiency boosts the healthcare sectors market share

Federated learning's application scope spans the entirety of AI for healthcare because it is a common study model that detaches the data pooling required for AI model evolution. Federated learning aids in acting and identifying clinically similar patients and forecasting hospitalizations due to cardiac events, impermanence and ICU stay time. Federated learning's applicability and benefits are revealed in medical imaging, specifically for entire-brain separation in MRI and brain tumor separation. Federated learning has an immediate clinical impact. Another area of influence is industrial research and relocation. For instance, in the case of a physician, by ensuring consistency of identification, they can improve their skills with specialist knowledge from other organizations. Similarly, patients in remote areas require medical awareness; federated learning may lower the barriers to becoming a data contributor; patients may be reassured that data remains with their organization and data access may be canceled.

In short, federated learning is guaranteed to acquire powerful, precise, protected, strong, and impartial models. It is possible to obtain information from global resources to care for patient's health. Federated learning impacts all collaborators and the entire treatment cycle and it is a possible impact on the exactness of medicine eventually, upgrading medical care is strongly assured. Therefore, the demand for federated learning in the medical sector is growing, boosting the market share for healthcare in the global federated learning market.

Market Geographical Share

Shortage of manpower and rapidly developing healthcare and biomedical sectors in Europe boost the region’s share in the global federated learning market

Due to the aging population and lack of healthcare professionals, the healthcare sector in Europe is incorporating AI into healthcare. Similarly, drug discovery, a tedious field requiring vast quantities of bioscience data regarding genomes and patents, among others from numerous biomedical journals and databases, could be revolutionized by employing federated learning. Since Europe is a leading market for healthcare, medical imaging, diagnostics, and drug development and has a shortage of human resources for carrying out various processes in the respective fields, large-scale adoption of AI incorporated with federated learning is widely employed in Europe. As a result, Europe dominates the regional segment of the global federated learning market.

Market Companies and Competitive Landscape

In terms of global and local producer numbers and strengths, the global federated learning market is saturated with local and global manufacturers such as NVIDIA, Cloudera, IBM, Microsoft, Google, Intel, IBM, Owkin, Intelligence, and Edge Delta. The market is fragmented and pivotal market stakeholders use market tactics such as mergers, acquisitions, product launches, contributions, and collaborations to gain a competitive advantage and recognition in their respective markets.

For instance, on April 23, 2021, IBM introduced updates to the company’s Watson AI, which finds business applications utilizing federated learning and time series capabilities. The new updates expand Watson’s tool collection, increasing their accuracy, privacy, and compliance. Similarly, on 29, 2021, NVIDIA introduced open-source software to incorporate a computing foundation for federated learning and accelerated AI in healthcare, financial services, and manufacturing.

NVIDIA

Overview: NVIDIA is a pioneer and leading chipset manufacturer whose chips finds applications in medium to high-end video cards. Apart from graphics cards and chips, the company manufactures SHIELD, a high-definition video streaming equipment. The company’s wide product portfolio could be accessed via the website or retail or online stores. Recently, NVIDIA acquired Arm for US$ 40 billion.

Product Portfolio:

• Clara Train SDK: The latest Clara Train SDK release, which includes Federated Learning (federated learning), makes it possible to collude safely, train, and contribute to a global model with NVIDIA EGX, the edge AI computing platform. Because only partial model weights from each site are shared with the global model, privacy is preserved and data is less vulnerable to model inversion. Federated Learning can be implemented in various distributed architectures, from peer-to-peer to cyclic and server-client. The implementation of NVIDIA Clara is based on a server-client approach, which means that a centralized server serves as a facilitator for the overall federated training with the involvement of various clients. Clara Train SDK's configurable MMAR (Medical Model ARchive) feature allows developers to bring their models and elements to perform Federated Learning and control whether the local training is run on a single GPU or various GPUs. The model is based on the BraTS (Multimodal Brain Tumor Segmentation) Challenge data, including 285 brain tumor patients. The findings associated with the respective research have already been presented at the Medical Image Computing and Computer-Assisted Intervention Society (MICCAI) conference in Shenzhen, China.

Key Development:

On September 15, 2021, NVIDIA and Kings’ College, London, partnered to develop medical imaging AI employing federated learning. As a part of the development of the respective project, the company trained a neural network for brain tumor segmentation. The technique can also share data between hospitals and researchers without compromising the patient’s privacy.