Federated Learning Challenges and Opportunities: An Outlook

TL;DR

This paper provides an outlook on federated learning, discussing key challenges and opportunities across five emerging directions, supported by practical insights from large-scale edge device systems.

Contribution

It offers a comprehensive categorization of current FL challenges and opportunities, highlighting practical perspectives and future research directions.

Findings

Identification of five key FL development directions

Practical observations from large-scale edge systems

Highlighting unaddressed challenges in FL

Abstract

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.