Inverse Distance Aggregation for Federated Learning with Non-IID Data
This addresses the challenge of non-IID data in federated learning for medical applications, but it appears incremental as it builds on existing FL methods with a new weighting approach.
The paper tackles the problem of statistical heterogeneity in federated learning for medical imaging by proposing Inverse Distance Aggregation (IDA), an adaptive weighting method for clients based on meta-information, and shows it outperforms Federated Averaging as a baseline.
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known FL approach, Federated Averaging as a baseline.