Keywords: Multi-tier Federate Learning, Personalization, Statistical Heterogeneity, Distributed Optimization, In-expensive Communication, Moreau Envelope
TL;DR: A personalized multi-tier federated learning using moreau envelope
Abstract: The challenge of personalized federated learning (pFL) is to capture the heterogeneity properties of data with in-expensive communications and achieving customized performance for devices. To address that challenge, we introduced personalized multi-tier federated learning using Moreau envelopes (pFedMT) when there are known cluster structures within devices. Moreau envelopes are used as the devices’ and teams’ regularized loss functions. Empirically, we verify that the personalized model performs better than vanilla FedAvg, per-FedAvg, and pFedMe. pFedMT achieves 98.30% and 99.71% accuracy on MNIST dataset under convex and non-convex settings, respectively.
Is Student: Yes