Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

TL;DR

Auto-FedAvg introduces a learnable, data-driven federated averaging method that dynamically adjusts aggregation weights based on data distribution and training progress, improving multi-institutional medical image segmentation.

Contribution

The paper proposes Auto-FedAvg, a novel federated learning algorithm with learnable aggregation weights that adapt to data heterogeneity and training dynamics.

Findings

Outperforms state-of-the-art FL methods on CIFAR-10 image recognition.

Effectively improves COVID-19 lesion segmentation in chest CT.

Enhances pancreas segmentation accuracy in abdominal CT.

Abstract

Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. However, non-identical data distribution across clients, known as the non-i.i.d problem in FL, could make this assumption for setting fixed aggregation weights sub-optimal. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models. We disentangle the parameter set into two parts, local model parameters and global aggregation parameters, and update them iteratively with a communication-efficient algorithm. We first show the validity of our approach by outperforming state-of-the-art FL methods for image recognition on a heterogeneous data split of CIFAR-10. Furthermore, we demonstrate our algorithm's effectiveness on two multi-institutional medical image analysis tasks, i.e., COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT.