Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis

• URL: http://arxiv.org/abs/2310.18346v2
• Date: Tue, 31 Oct 2023 09:13:18 GMT
• Title: Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis
• Authors: Ming Li and Guang Yang
• Abstract summary: Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. 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.
• Score: 11.412151951949102
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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