FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data

TL;DR

FedSiam-DA introduces a dual-aggregated contrastive federated learning method using Siamese networks to effectively personalize models and improve global model generalization under data heterogeneity.

Contribution

It proposes a novel dual-aggregated contrastive federated learning approach with dynamic weighting and Siamese networks for personalized and robust federated learning.

Findings

Outperforms previous FL methods on benchmark datasets.

Effectively personalizes local models under data heterogeneity.

Enhances global model generalization with dual-aggregation.

Abstract

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the siamese network, FedSiam-DA regards the local and global model as different branches of the siamese network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets.