FEDERATED LEARNING CHALLENGES AND OPPORTUNITIES: AN OUTLOOK

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled Frontiers of Federated Learning: Applications, Challenges, and Opportunities. The outlook is categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.

Automated summary

This paper provides an outlook on the development of federated learning (FL) and highlights five emerging directions, including algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. The perspectives shared in this paper are based on practical observations from large-scale federated systems for edge devices.