Influence of Federated Learning on Contemporary Research and Applications

Federated Learning (FL) is a rather new distributed machine learning paradigm based on a collaboratively decentralized privacy-preserving technology. It supports a range of multiple clients (starting from “simple” mobile devices but also including organizations, institutions, etc.) coordinated decentralized machine learning by one or more central servers. As it is a rather new approach various strategies for organization of multiple clients and implementation reliable environments have been developing. The efficiency of selected FL strategy for a particular problem is influenced by involved actors and organizational structure. Particular attention within FL should be paid to adoption of data privacy and security aspects. FL is powerful and widely applicable in areas like banking and finances, automotive industry, IoT and smart environments, health and medicine etc.. In this paper we will pay attention to several crucial aspects of FL, present its essential characteristics, and briefly illustrate several characteristic applications.