Personalized Multi-tier Federated Learning

Sourasekhar Banerjee, Alp Yurtsever, Monowar Bhuyan

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

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/personalized-multi-tier-federated-learning/code)