Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

TL;DR

This paper introduces FedU2, a novel federated unsupervised learning method that improves representation quality in non-IID data settings by using regularizers and aggregators to prevent collapse and unify representations.

Contribution

FedU2 is a new framework that enhances representation diversity and consistency in federated unsupervised learning with non-IID data.

Findings

FedU2 outperforms existing methods on CIFAR10 and CIFAR100 datasets.

The proposed approach effectively prevents representation collapse.

FedU2 achieves more unified and robust representations across clients.

Abstract

Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existing FUSL methods suffers from insufficient representations, i.e., (1) representation collapse entanglement among local and global models, and (2) inconsistent representation spaces among local models. The former indicates that representation collapse in local model will subsequently impact the global model and other local models. The latter means that clients model data representation with inconsistent parameters due to the deficiency of supervision signals. In this work, we propose FedU2 which enhances generating uniform and unified representation in FUSL with non-IID data. Specifically, FedU2 consists of flexible uniform regularizer (FUR) and efficient unified aggregator (EUA). FUR in each client avoids representation collapse via dispersing samples uniformly, and EUA in server promotes unified representation by constraining consistent client model updating. To extensively validate the performance of FedU2, we conduct both cross-device and cross-silo evaluation experiments on two benchmark datasets, i.e., CIFAR10 and CIFAR100.