Improving Performance of Private Federated Models in Medical Image Analysis

Federated learning (FL) is a distributed machine learning (ML) approach that allows data to be trained without being centralized. This approach is particularly beneficial for medical applications because it addresses some key challenges associated with medical data, such as privacy, security, and data ownership. On top of that, FL can improve the quality of ML models used in medical applications. Medical data is often diverse and can vary significantly depending on the patient population, making it challenging to develop ML models that are accurate and generalizable. FL allows medical data to be used from multiple sources, which can help to improve the quality and generalizability of ML models. Differential privacy (DP) is a go-to algorithmic tool to make this process secure and private. In this work, we show that the model performance can be further improved by employing local steps, a popular approach to improving the communication efficiency of FL, and tuning the number of communication rounds. Concretely, given the privacy budget, we show an optimal number of local steps and communications rounds. We provide theoretical motivations further corroborated with experimental evaluations on real-world medical imaging tasks.