Federated learning (FL) enhances data privacy and compliance with data regulations by enabling multiple decentralized parties to collaboratively train machine learning models without sharing their data. This makes it an ideal paradigm for the healthcare domain. Despite its inherent privacy-by-design, FL faces performance and convergence challenges when dealing with non-independent and identically distributed (non-IID) data. Although previous studies have primarily addressed the challenges of skewed label distribution across clients, which are more effective for classification, we focus in this paper on the much less explored challenge of multi-domain FL in medical image segmentation tasks, where client data originate from different domains with varying feature distributions. To address this problem, we propose FedGA, which performs heuristic, gradient-free optimization on the server side after local model aggregation in order to optimize the global model by using a genetic algorithm. Empirical results on breast lesion segmentation from ultrasound images and prostate segmentation from T2w MRI images show the potential of FedGA in federated learning schemes. Specifically, FedGA improves the segmentation precision in critical boundary regions compared to existing approaches, accelerates global model convergence, reduces the total number of communication rounds required to achieve optimal performance, and offers better overall efficiency.
Ahmed et al. (Thu,) studied this question.