用因果潜变量扩散模型实现多任务医疗图像分割，兼顾隐私与性能
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
- 结合因果表示学习与潜变量扩散模型，优化多任务联邦学习
- 在多个医学图像数据集上分割精度显著优于基线方法
- 有效降低模型分界点的信息泄露，适合医疗隐私敏感场景
联邦学习通过聚合客户端模型更新实现去中心化训练，避免原始数据共享；分割联邦学习进一步将模型分置客户端与服务器之间，降低客户端计算和通信开销。然而，真实医疗机构通常不执行单一任务，标准联邦学习和分割联邦学习难以匹配临床实际流程。多任务联邦学习虽允许多任务协作，但常引发训练不稳定和隐私风险。本文提出MuCALD-SplitFed，一种融合因果表示学习与潜变量扩散的多任务分割联邦学习框架。实验表明，该方法在多个医学图像分割任务中稳定收敛，显著优于基线分割联邦学习。所提方法有效减少模型分界处的信息泄露，在重建攻击和成员推断攻击下表现更优。此外，其性能超越现有先进个性化联邦学习与多任务联邦学习方法。代码已开源：https://github.com/ChamaniS/MuCALD_SplitFed。
原文摘要 · Abstract (English)
Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning further partitions the model between clients and a server to reduce computation and communication at the client side. However, decentralized medical institutions rarely operate on a single shared task, making standard Federated and SplitFed collaborations poorly aligned with real clinical workflows. Multi-task FL extends these frameworks by allowing clients to handle different tasks, but often introduces instability and privacy vulnerabilities. This study proposes \textbf{MuCALD-SplitFed}, a multi-task SplitFed framework that integrates causal representation learning and latent diffusion. Experiments show MuCALD-SplitFed consistently improves segmentation, while baseline SplitFed fails to converge. The proposed approach further reduces information leakage at split points, mitigating reconstruction-based and membership inference attacks. Additionally, MuCALD SplitFed outperforms state-of-the-art personalized FL and multi-task FL approaches. The code repository is: https://github.com/ChamaniS/MuCALD_SplitFed.
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