Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.

翻译：在大型多中心、多扫描仪场景下的胸部疾病分析常受限于严格的隐私政策。联邦学习（FL）提供了一种潜在解决方案，但传统的参数化FL可能受到高通信成本、数据泄露和异质性等问题的限制。基于蒸馏的FL可提升效率，但其依赖于代理数据集，这一要求在临床实践中往往不切实际。为应对这些挑战，我们提出了一种基于数据无蒸馏的FL方法FedKDF。在FedKDF中，服务器采用轻量级生成器聚合来自不同客户端的知识，无需访问其私有数据或代理数据集。FedKDF将客户端的预测器整合为单一统一预测器，并通过轻量级生成器中学习到的知识对其进一步优化。实验结果表明，FedKDF为高效、隐私保护的联邦胸部疾病分析提供了一种稳健解决方案。