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

Understand

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.