针对医疗联邦学习中的数据异构问题，提出鲁棒聚类算法提升模型稳定性。
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
- 用Wasserstein距离构建客户端分布的不确定性集，捕捉数据漂移风险。
- 通过整数分数规划优化聚类分组，降低异构数据导致的模型偏差。
- 适用于医疗等隐私敏感场景，保障客户端分布隐私。
本文针对跨孤岛联邦学习中数据分布异构的挑战，提出一种新算法——跨孤岛鲁棒聚类联邦学习（CS-RCFL）。该方法利用Wasserstein距离在每个客户端的经验分布周围构建模糊集，以捕获局部数据可能发生的分布偏移，从而评估最坏情况下的模型性能。随后，我们设计了一种模型无关的整数分数规划方法，用于确定最优的分布鲁棒聚类分组，避免由统计异构客户端数据引起的局部模型偏差，并对线性与逻辑回归模型进行了分析。最后，我们讨论了一种确保客户端分布隐私的联邦学习协议，这对于医疗等机构作为客户端的场景至关重要。我们在合成数据和真实医疗数据上对算法进行了评估。
原文摘要 · Abstract (English)
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserstein distance to construct ambiguity sets around each client's empirical distribution that capture possible distribution shifts in the local data, enabling evaluation of worst-case model performance. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions so that possible biases in the local models caused by statistically heterogeneous client datasets are avoided, and analyze our method for linear and logistic regression models. Finally, we discuss a federated learning protocol that ensures the privacy of client distributions, a critical consideration, for instance, when clients are healthcare institutions. We evaluate our algorithm on synthetic and real-world healthcare data.
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