CMFL: Mitigating Communication Overhead for Federated Learning

Federated Learning enables mobile users to collaboratively learn a global prediction model by aggregating their individual updates without sharing the privacy-sensitive data. As mobile devices usually have limited data plan and slow network connections to the central server where the global model is maintained, mitigating the communication overhead is of paramount importance. While existing works mainly focus on reducing the total bits transferred in each update via data compression, we study an orthogonal approach that identifies irrelevant updates made by clients and precludes them from being uploaded for reduced network footprint. Following this idea, we propose Communication-Mitigated Federated Learning…