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

arXiv:2607.28338 · cs.LG, cs.CR, cs.DC, stat.ML · Submitted 2026-07-30 · Read on arXiv

Michael Ben Ali, Imen Megdiche, André Péninou, Olivier Teste

cs.LG, cs.CR, cs.DC, stat.ML

Submitted: 2026-07-30

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 85× in this paper · explained in 2 episodes
• Federated Learning — 31× in this paper · explained in 19 episodes
• Accuracy — 29× in this paper · explained in 1 episode
• Clustered Federated Learning — 15× in this paper
• Homomorphic Encryption — 12× in this paper
• Robustness — 6× in this paper · explained in 6 episodes
• Non-IID Data — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Hyperparameters — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Davies-Bouldin Index — 3× in this paper
• Differential Privacy — 3× in this paper · explained in 5 episodes
• Distribution Shift — 3× in this paper · explained in 12 episodes
• Ablation Study — 2× in this paper · explained in 3 episodes
• Adjusted Rand Index — 2× in this paper
• Computational Efficiency — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Dimensionality Reduction — 2× in this paper · explained in 2 episodes
• Federated Learning (FL) — 2× in this paper · explained in 12 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Communication Efficiency — 1× in this paper · explained in 2 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Differential Privacy (DP) — 1× in this paper · explained in 12 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Distribution Shifts — 1× in this paper · explained in 2 episodes
• Gaussian Mixture Model — 1× in this paper
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• In Secure Multi-Party Computation — 1× in this paper
• Quantization — 1× in this paper · explained in 15 episodes
• ReLU Network — 1× in this paper · explained in 2 episodes
• ReLU Neural Network — 1× in this paper
• Variance Reduction — 1× in this paper · explained in 2 episodes

Sources

• A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications
• Robust Federated Learning in a Heterogeneous Environment

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