The federated learning market was USD 128.3 million in 2023, which will increase to USD 260.5 million, progressing at a 10.7% compound annual growth rate, by 2030.
This growth is because of the increasing acceptance of this learning solution across different sectors to improve understanding of organizations and devices, and the growing funding in drug discovery.
Furthermore, its beneficial features like better data privacy, compared to conventional learning, make it exclusive. It enables numerous companies to produce strong deep training models without sharing real data from devices. Therefore, sustaining data confidentiality, protection, convenience to grand data sets to be worked on, and privileged access to data.
The drug discovery category, based on application, was a significant contributor to the industry. This is because of the rising occurrence of illnesses worldwide owing to the mounting populace, decreasing nutritious quality of food, and increasing levels of pollution.
Whereas, the industrial IoT category accounted for a 20% share, in 2023. This can be attributed to the benefits, including enhanced productivity, ease of addition of new business models, and lowered prices, provided by federated learning united with IoT.
Furthermore, the increasing advancement in technology in developed and developing nations and the rising acceptance of big data analytics further boost this category's growth.
In 2023, the automotive & transportation category, based on vertical, was the largest contributor to the federated learning market, with a 35% share. This is because of the increasing sales of vehicles globally, owing to the rising economies and the mounting populace. This results in further traffic on roads and eventually congestion, and people spend hours facing traffic.
This boosts the requirement for federated learning, which with the support of ML and AI algorithms, can assist in managing traffic by adjusting routes and creative solutions for vehicles.
Europe led the industry in 2023, with a 50% share, and it will remain leading throughout this decade. This will be because of the stringent privacy & protection guidelines employed by governments under the General Data Protection Regulation.
Moreover, data privacy is an important feature of federated learning, which complies with these guidelines, therefore, making federated learning an attractive method for businesses in this region.
North America is likely to advance at a considerable rate in the years to come. This will be mainly because of the existence of recognized generated learning solution vendors in this continent.
Furthermore, the extensive funding in R&D particularly in AI and IoT for developing and enhancing FL algorithms and techniques further boosts the industry expansion.
With the increase in the funding for drug discovery, the federated learning industry will progress continuously in the years to come.
