As per the report published by Fior Markets, the Federated Learning Solutions market is expected to grow from USD 91.5 Million in 2020 to USD 210.86 Million by 2028, at a CAGR of 11% during the forecast period 2021-2028.
The growing need for various companies to improve their learning will drive market growth during the coming years. In recent years, the emergence of AI, connected devices, and big data analytics has resulted in a massive increase in the market for federated learning solutions. Firms utilize federated learning to train algorithms on a variety of datasets without transferring data, which will boost the federated learning solutions industry’s growth throughout the forecasted timeframe. Over the forecast period, the growing usage of cloud technology is anticipated to pave the way for the market for federated learning solutions to develop. On the other hand, there is a scarcity of qualified technical expertise, which could hinder the market growth.
Federated learning is a distributed Machine Learning (ML) technique that trains systems using decentralized data. Rather than gathering data on a single server or data lake, data is collected locally on smartphones, industrial sensing devices, and other edge gadgets, and models are trained on-device. The trained models are merged and transmitted to a central server. The decision to transport models rather than data has a number of implications and compromises. Federated learning is a novel technology method that is still in its early stages of development. Verticals are focused on data security, hyper-personalization, and contextual recommendation, which will be critical in boosting app usage and eCommerce sales; federated learning is likely to play a significant part in this.
Edge AI software and unattended machine learning are likely to drive the federated learning solutions market. Major corporations are researching federated learning, which is essential in enabling privacy-sensitive applications where training data is dispersed at the edge. By sharing model changes, federated learning takes a step toward securing consumers’ data. Data privacy and security are becoming increasingly important for businesses. The federated learning solution has created a new paradigm for data-driven applications. Data silos and an emphasis on data privacy are now major AI issues, but federated learning might be a solution. It may provide a unified paradigm for numerous businesses while protecting local and sensitive data, allowing them to benefit from each other without having to worry about data security. These factors can drive the market growth. However, the scarcity of a skilled workforce, particularly IT specialists, is a major obstacle that most businesses face when implementing machine learning into their business operations, which can hinder the market growth. Employees face difficulties grasping and executing federated learning models for training data since it is a novel idea. This is due to a lack of training for staff on how to use the federated learning models.
Key players operating in the global Federated Learning Solutions market include Amazon Redshift, Cloudera (US), Consilient (US), DataFleets (US), Decentralized Machine Learning (Singapore), Edge Delta (US), Enveil (US), Google (US), IBM (US), Inzata, Intelligence (UK), Lifebit (UK), Microsoft (US), NVIDIA (US), OmniSci, Owkin (US), SambaNova Systems, Secure AI Labs (US), Sherpa.ai (Spain), Snowflake, Teradata Vantage, and Vertica.
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The healthcare and life sciences segment dominated the market and held the largest market share of more than 18% in the year 2020
On the basis of vertical, the global Federated Learning Solutions market is segmented into healthcare and life sciences; manufacturing; e-commerce and retail; banking, financial services, and insurance; energy and utilities; telecommunications and IT; media and entertainment; and government. The healthcare and life sciences segment dominated the market and held the largest market share of more than 18% in 2020. Furthermore, during the projected years, the manufacturing vertical is predicted to increase at the fastest CAGR. With the growth in competitiveness and increased attention on the Industrial Internet of Things (IoT), industrial businesses are emphasizing data analysis from a variety of sources, including the web, mobile, shops, and social media.