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 ...
| Authors | Michael Ben Ali, Imen Megdiche, André Péninou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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