Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

2026-07-30

AI Summary

This work addresses the trilemma in clustered federated learning—where privacy preservation, communication efficiency, and computational scalability are difficult to achieve simultaneously—by proposing an encryption-compatible, highly efficient solution. The approach reformulates metadata-driven clustering as a distributed expectation-maximization (EM) process that relies solely on additive operations at the server, thereby enabling seamless integration with practical privacy-enhancing technologies such as additive homomorphic encryption. For the first time, this method breaks the CFL trilemma without compromising efficiency, significantly improving client model performance across diverse heterogeneous datasets while simultaneously ensuring strong privacy guarantees, low computational overhead, and high communication efficiency.

Abstract

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.

Problem

• Clustered Federated Learning
• privacy preservation
• metadata
• communication efficiency
• computational efficiency

Innovation

• Clustered Federated Learning
• Expectation-Maximization
• Metadata
• Privacy-Preserving
• Secure Aggregation