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.

翻译：联邦学习（FL）是一种分布式机器学习（ML）方法，允许在不集中数据的情况下进行训练。该方法对医学应用尤其有益，因为它解决了与医学数据相关的一些关键挑战，如隐私、安全和数据所有权。此外，FL可以提升医学应用中ML模型的质量。医学数据往往多样化，且因患者群体的不同而差异显著，这使得开发准确且具有泛化能力的ML模型具有挑战性。FL允许使用来自多个来源的医学数据，这有助于提高ML模型的质量和泛化能力。差分隐私（DP）是一种主流的算法工具，用于确保该过程的安全性和隐私性。在本工作中，我们证明通过采用局部步骤（一种提高FL通信效率的流行方法）并调整通信轮数，可以进一步提升模型性能。具体而言，在给定隐私预算的情况下，我们展示了局部步骤和通信轮数的最优数量。我们提供了理论依据，并通过真实医学成像任务的实验评估进一步验证了这些结论。