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

by   Yingda Xia, et al.

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.


Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that...

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

Federated learning (FL) has been widely employed for medical image analy...

Federated Cross Learning for Medical Image Segmentation

Federated learning (FL) can collaboratively train deep learning models u...

ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation

Chest Computational Tomography (CT) scans present low cost, speed and ob...

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Federated learning (FL) for medical image segmentation becomes more chal...

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

The purpose of federated learning is to enable multiple clients to joint...

Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

Collaboration among multiple data-owning entities (e.g., hospitals) can ...