Federated Learning Market

Updated on : March 3, 2023

Federated Learning Market Analysis

As per the AS-IS scenario, the global federated learning market is expected to grow from $127 million in 2023 to $210 million by 2028, at a CAGR of 10.6% during the forecast period. The major growth factor of the federated learning market is that federated learning allows numerous players to develop strong deep training models while sharing information, permitting crucial concerns such as data protection, confidentiality, privileged access to information, and accessibility to large datasets to be addressed.

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Federated Learning Market Dynamics

Driver: Growing adoption of federated learning in various applications for data privacy

The way ML methods are taught is changing due to federated learning. Companies are concentrating their efforts on doing an in-depth study on federated learning. Organizations can strengthen current algorithms and enhance their AI applications by using federated learning. The demand for more learning among gadgets as well as organizations is rising. In the wellness sector, federated learning might aid healthcare workers in providing good quality outcomes and accelerating medication development. For instance, a new peer-to-peer approach called FADNet is a solution to address centralized learning deficits. This approach does not need a centralized system for learning; instead, every client learns its information, as well as the outcome is sent to another user via a cycle of aggregating. They can obtain true traffic and carparks situations using active learning and federated learning, which are not always straightforward to confirm via modelling. This method decreases labeling time and cost while also improving the whole CI/CD workflow and installation of the self-driving vehicle machine learning algorithms. This method needs greater computer capacity, unlike federated learning which is centralized or server-based. The system may be trained with dispersed car or network sensors readings by integrating federation and learning approach. Regulations include federated learning spanning departments within just the firm, often drives federated learning within a business or firm. Laws, such as federated learning among departments of a firm, often drive federated learning within a business or firm. In some circumstances, though, rules may compel organizations to give their information. For instance, Google has introduced its first production-level federated learning platform, which will produce several federated learning-based applications, such as on-device item ranking, next-word prediction, and content suggestion.

Restraint: Lack of skilled technical expertise

The lack of skilled workers, specifically IT professionals, is a significant impediment which many firms face when adopting ML into existing workflows. Challenging for staff to grasp and implement federated learning systems as it is a revolutionary idea. Owing to a dearth of competent personnel to build and implement federated learning tasks which entail complicated methods such as machine learning, recruiting, and keeping technological capabilities became a significant concern for some businesses. As an organization, they need to build increasingly skillset and job titles. For instance, Engineers that can manage and comprehend the modern federated learning infrastructure associated with installation and maintenance machine learning algorithm are needed by organisations. Data scientists are most highly trained scientific specialists, having extensive understanding of computer science, statistics, and conceptual understanding. Qualified data scientists, on the other hand, demanding hefty fees and require successful things, which are often out of reach for SMEs or even major corporations. To stay relevant in a marketplace with limited ability, there is a growing demand of federated learning modules throughout sectors. As a result, the existing shortage of trained individuals is a key barrier for the worldwide federated learning solutions industry.

Opportunity: Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally

Federated learning is a method of training ML algorithms on decentralized data. Rather than storing data from a single computer or data mart, information is kept at original sources such as smartphones, manufacturing detecting gear, as well as other end devices, and the ML machines are trained on-the-fly. This helps make decisions before sending back to a centralized computer. For instance, this financial sector makes good use of federated learning for debt risk analysis. Typically, banks utilize whitelisting procedures to exclude consumers based on card details from federal reserve. By partnering with the other financial organizations and eCommerce enterprises, risk assessment variables, such as taxation and reputation, may be used. Because sharing personal customer data across firms is dangerous, firms may utilize federated learning to create a risk evaluation ML model. In another instance, when an eCommerce start-up seeks to create a model to assess its clients' likelihood to purchase a thing, it uses the data from its website or application to run simulations. Spending time on a manufacturer's website, items bought simultaneously, goods viewed but not bought, as well as other customer information are examples of this type of information. Each client has around 50 and 1,000 data points covering a period. This data is analyzed and transferred to a centralized data center or devices for processing. Information is vulnerable to hackers because of such a data transit. ML procedures are distributed using the federated learning method. It allows businesses to acquire a common paradigm in a cooperative way by sharing as well as storing data from the device.

Challenge: System integration and interoperability issue

Customers with varying computation and network control create system diversity. The sustainability of federated algorithms is harmed by such variability in personal computer rates, which leads to a considerable reduction in theoretical execution. Federated learning is using distant computers and walled storage systems to build predictive methods yet maintaining information locally, including cell devices or institutions. Education in heterogeneous and possibly huge networks posed new issues that necessitated a radical shift away from traditional techniques to huge ML, dispersed optimization, and data processing while maintaining privacy. Because of differences in technology internet connection (3G, 4G, 5G, and Wi-Fi) and energy, every item in federation systems may have different storing, computing, and communication networks. With the million-device network, the internet backbone capacity and framework restraints on every unit. This diversity that exists in multiple hardware requirements and variable conditions across participating devices is a major challenge in federated learning. In theory, heterogeneity can have a significant impact on the federated learning training phase, such as rendering a unit inaccessible for learning or preventing it from uploading model updates.

As per optimistic scenario, among verticals, the automotive and transportation segment to grow at a the highest CAGR 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, automotive and transportation, IT and telecommunication and other verticals (government, and media and entertainment). As per optimistic scenario, the automotive and transportation vertical is expected to grow at the highest CAGR during the forecast period. Autonomous vehicles have a complicated technology. Observation, forecasting, monitoring, localization, modelling, interfaces using public cloud, and data management are among the technology used in the system. With the introduction of automated vehicles, the focus was on data, edge-to-edge computer technology handling, and improved ML algorithm in addition to making automated vehicles reliable and secure for seamless integration through one area of the globe to another, even as analyzing information and personal confidentiality wirelessly. Effective learning chooses the most relevant pieces of data to classify and add to the instructional pool, which is expected to drive the growth of federated learning in the vertical.

As per optimistic scenario, among regions, Europe to hold the largest market size during the forecast period

As per optimistic scenario, Europe is estimated to account for the largest market size in the federated learning market during the forecast period. The federated learning market for healthcare is categorized into various applications, such as patient data and risk analysis, medical imaging and diagnostics, precision medicine, lifestyle management and monitoring, drug discovery, inpatient care and hospital management, virtual assistant, wearables, and research. The drug discovery process is tedious, which requires the researcher to analyze vast quantities of bioscience information, including patents, genomic data, and a large number of publications uploaded daily across all biomedical journals and databases. This makes it necessary to evolve the drug discovery process, and federated learning has the capability to influence and enhance this process. Hence, to offer better platform across the market vendors in the market are developing new products. In Europe, the challenges linked to aging populations and shortages of healthcare professionals are driving up the adoption of AI technologies in healthcare. This is, in turn, driving the federated learning market growth in Europe.

Federated Learning Market - Key Players

The federated learning solutions vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global federated learning solutions 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).

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Scope of the Report

This research report categorizes the Federated Learning Market based on application, vertical, and region.

Market 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
• Other Applications

Market By Verticals:

• Banking, Financial Services, and Insurance
• Healthcare and Life Sciences
• Retail and Ecommerce
• Manufacturing
• Energy and Utilities
• Automotive and Transportaion
• IT and Telecommunication
• Other Verticals

Market By Region:

• North America
• Europe
• APAC
• MEA
• Latin America

Federated Learning Market Recent Developments:

• In March 2022, NVIDIA launched Communications Intelligence Platform, a Clara Holoscan, solution which was designed for its healthcare sciences business has been updated to MGX, as a one-of-a-kind end-to-end system for both AI technologies and intelligent healthcare manufacturing and deployment in implantable augmentations.
• In January 2022, Intel launched OpenVINO integration with TensorFlow, the OpenVINO toolkit is used for online improvements and execution required for increased TensorFlow interoperability. It was created for programmers who want to try out the OpenVINO toolset and see how it might assist them boost the effectiveness of existing inferential apps with little code changes.
• In November 2021, NVIDIA launched NVIDIA FLARE, NVIDIA FLARE stands for Federated Learning Application Runtime Environment is an open-source platform, which is based on the foundation of NVIDIA Clara Train's federated learning software, and was employed for biomedical imagery, functional genomics, cancer, and COVID-19 research. Investigators and data scientists could use this SDK to convert their current ML techniques processes to a decentralized network. NVIDIA FLARE supports a variety of networked topologies, spanning peer-to-peer, asynchronous, and server-client techniques, among others.
• In June 2021, NVIDIA enhanced NVIDIA Clara, In NVIDIA Clara Train 4.0, a new tool named Homomorphic Encryption (HE) was added. It enables users to compute encrypted data. With Clair Train 4.0 the communication channel is established using SSL certificates and provisioning tool. For instance, NVIDIA used the TenSEAL module from OpenMined, a concise Python wrapper over Microsoft SEAL, to provide safe aggregating through federated learning by using HE.
• In February 2021, Enveil introduced new version of ZeroReveal 3.0. It delivers the homomorphic encryption-powered capabilities through an efficient and decentralized framework designed to reduce risk and address business challenges, including data sharing, collaboration, monetization, and regulatory compliance. The solution enhancements delivered in 3.0 releases strengthen integration, performance, and user experience features for both Enveil’s ZeroReveal Search and ZeroReveal Machine Learning product lines.

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