Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity

TL;DR

This paper introduces a personalized federated learning approach using hybrid and unstructured pruning to create client-specific subnetworks, improving performance under data heterogeneity.

Contribution

It proposes a novel method combining structured and unstructured pruning to derive personalized models for clients in federated learning, addressing data heterogeneity.

Findings

Clients with similar data share similar subnetworks

Personalized subnetworks improve local client performance

The method outperforms traditional federated learning approaches

Abstract

The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server. However, learning a single global model might not work well for all clients participating in the FL under data heterogeneity. Therefore, the personalization of the global model becomes crucial in handling the challenges that arise with statistical heterogeneity and the non-IID distribution of data. Unlike prior works, in this work we propose a new approach for obtaining a personalized model from a client-level objective. This further motivates all clients to participate in federation even under statistical heterogeneity in order to improve their performance, instead of merely being a source of data and model training for the central server. To realize this personalization, we leverage finding a small subnetwork for each client by applying hybrid pruning (combination of structured and unstructured pruning), and unstructured pruning. Through a range of experiments on different benchmarks, we observed that the clients with similar data (labels) share similar personal parameters. By finding a subnetwork for each client ...