FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

by   Yichen Ruan, et al.
Carnegie Mellon University

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well.


page 1

page 2

page 3

page 4


FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution

Classical federated learning approaches yield significant performance de...

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

Federated Learning (FL) enables the multiple participating devices to co...

Parameterized Knowledge Transfer for Personalized Federated Learning

In recent years, personalized federated learning (pFL) has attracted inc...

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Clustered federated Multitask learning is introduced as an efficient tec...

Visual Prompt Based Personalized Federated Learning

As a popular paradigm of distributed learning, personalized federated le...

Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

Clustered Federated Multitask Learning (CFL) was introduced as an effici...

Kernel-based Federated Learning with Personalization

We consider federated learning with personalization, where in addition t...

Please sign up or login with your details

Forgot password? Click here to reset