As per AS-IS scenario, the global Federated Learning Solutions Market size to grow from USD 117 million in 2023 to USD 201 million by 2028, at a Compound Annual Growth Rate (CAGR) of 11.4% during the forecast period. Various factors such as the potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices and the capability to enable predictive features on smart devices without impacting user experience and leaking private information are expected to offer growth opportunities for federated learning solutions during the forecast period.
As per AS-IS scenario, among verticals, the manufacturing segment to grow at the highest CAGR during the forecast period
The federated learning solutions market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, and other verticals (telecommunications and IT, 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. Moreover, the manufacturing vertical is expected to grow at the highest CAGR during the forecast period. With the increasing focus on Industrial Internet of Things (IIoT) and the rise in competition, manufacturing companies are prioritizing the analysis of data collected from numerous sources, including web, mobile, stores, and social media.
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Federated learning is an innovative ML approach with a number of research projects to identify different applications across verticals. In federated learning, a server coordinates a network of nodes, each of which has training data that it cannot or will not share directly. The nodes train a model independently, and the models are shared with the server. 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. The federated learning solutions market is expected to be in line with technologies such as edge AI software and unsupervised ML.
Some of the key players operating in the federated learning solutions market include NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Owkin (US), Intellegens (UK), DataFleets (US), Edge Delta (US), Enveil (US), Lifebit (UK), Secure AI Labs (US), Sherpa.ai (Spain), Decentralized Machine Learning (Singapore), and Consilient (US). These federated learning solutions vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global federated learning solutions market.
NVIDIA was incorporated in 1993 and is headquartered in California, US. The company designs Graphics Processing Units (GPUs) for the gaming and professional markets, as well as systems on some chip units for the mobile computing and automotive market. NVIDIA is a computing platform company, innovating at the intersection of graphics, HPC, and AI. The company specializes in the manufacturing of graphics-processor technologies for workstations, desktop computers, and mobile devices. The company is a major supplier of integrated circuits used for personal-computer motherboard chipsets, GPUs, and game-consoles. NVIDIA uses federated learning to train a neural network for brain tumor segmentation. The technique allows data-sharing between hospitals and researchers while preserving patient privacy. 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 over USD 24 billion in Research and Development (R&D). It operates through two business segments: graphics segment and compute and networking segment. NVIDIA offers the NVIDIA Clara platform, which is a healthcare application framework for AI-powered imaging, genomics, and the development and deployment of smart sensors. With its federated learning feature, the platform enables different sites to securely collaborate, train, and contribute to a global model.
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Cloudera was founded in 2008 and is headquartered in California, US. It delivers enterprise data cloud from the edge to AI that comprises Cloudera DataFlow (CDF), Cloudera Data Warehouse, and Cloudera ML offerings, helping businesses analyze data for gaining actionable data insights. In January 2019, Cloudera and Hortonworks merged to deliver the enterprise data cloud platform to enterprises. The open-source enterprise data cloud platform would provide enterprises with the flexibility of running data on both hybrid and multi-cloud environments. It caters to various industries, including financial services, manufacturing, insurance, telecommunication, retail, technology, healthcare, energy and utilities, the public sector, and education. Cloudera provides its offerings to a broad customer base comprising Magenta Telekom, Micron, Bombay Stock Exchange, Komatsu Mining Group, Airtel, Intel, and Yes Bank. It has a presence in South America, North America, EMEA, and APAC. Cloudera Fast Forward Research Labs in 2018 wrote a research report about federated learning. The research lab introduced the report on federated learning to predict industrial equipment failure in their interactive prototype Turbofan Tycoon.
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