mlsanityData to 14 Oct 2026

RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform

arXiv 2610.03054 ↗First posted 2 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster, mitigating client drift that degrades single-model methods under non-IID data. Prior methods discover cluster structure inside the training loop through gradient similarity, loss evaluation, or EM-style updates, thus increasing communication overhead, exposing gradients to inversion attacks, and providing no mechanism to assign clients absent from training. We propose RIPPLE, a clustered FL framework in which cluster assignment is computed entirely offline from a spectral characterization of each client's local data: a variance-weighted principal-component prototype embedded via the Wavelet Scattering Transform and decoded by a Gaussian Mixture VAE trained server-side on synthetic client populations before federation begins. Per-round communication cost matches FedAvg exactly, and a client absent from training obtains a personalized model from a single forward pass, without gradient computation, model evaluation, or extra communication round. We prove that the gap between RIPPLE's surrogate clustered objective and the oracle is bounded by a computable quantity decaying with client sample size and independent of federation duration; per-cluster convergence matches the minimax-optimal rate for non-convex smooth objectives. Across five benchmarks spanning controlled and realistic heterogeneity, RIPPLE consistently outperforms all baselines, with margins growing on the most realistic partitions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 2 Oct 2026.

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32 of 50 references.

References · 32

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20 more
  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
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  8. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
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  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
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  15. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 24
  16. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 9
  17. 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learningcited by 5
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  19. 2024Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learningcited by 4
  20. 2025Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learningcited by 2

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