  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, 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.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing