这篇论文提出了FLAMECHE，让聚类联邦学习也能用上加密保护，不用再牺牲隐私换效率。
FLAMECHE是一种新的聚类联邦学习方法，通过将元数据聚类重构为分布式期望最大化过程，使服务器更新仅依赖加法运算，从而兼容标准联邦学习的加密机制。该方法在多个数据集和异构场景下的实验表明，它提升了客户端模型的有效性，同时缓解了隐私保护、通信成本和计算效率之间的CFL三难困境。
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effective training. Existing methods face a trade-off between privacy preservation, communication cost, and computational efficiency. We formalize this as the CFL trilemma, according to which improving two of these dimensions comes at the expense of the third. A prominent paradigm relies on metadata (i.e., low-dimensional representations of client datasets shared with the server) to enable communication- and computation-efficient clustering. However, such approaches are not compatible with standard FL privacy-preserving mechanisms. To address this limitation, we propose FLAMECHE, which reformulates metadata-based CFL as a distributed Expectation-Maximization (EM) procedure, restricting server updates to additive operations while preserving efficiency. This design enables compatibility with practical secure FL schemes. We conducted extensive experiments on multiple datasets under various heterogeneous scenarios. Results show that FLAMECHE improves the effectiveness of client models. It enables encryption-compatible metadata-based clustering, enhancing its positioning within the CFL trilemma.