  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.
