Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Abstract

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as lack of convergence and potential for catastrophic forgetting in federated learning across real-world heterogeneous devices. In this paper, we demonstrate that attention-based architectures (e.g., Transformers) are fairly robust to distribution shifts and hence improve federated learning over heterogeneous data. Concretely, we conduct the first rigorous empirical investigation of different neural architectures across a range of federated algorithms, real-world benchmarks, and heterogeneous data splits. Our experiments show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. We will release our code and pretrained models at https://github.com/Liangqiong/ViT-FL-main to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front.

On the Importance and Applicability of Pre-Training for Federated Learning

“…To our knowledge, very few works have studied this [55,73]; none are as systematic as ours on the effect of pre-training. [67,38,15] used pre-trained models in their experiments but did not or only briefly analyze their effect.…”