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arXiv Machine Learning

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

RIPPLE is a clustered federated learning framework that assigns clients to groups based on an offline spectral characterization of their local data. It uses a variance‑weighted principal‑component prototype embedded via the Wavelet Scattering Transform and decoded by a Gaussian Mixture VAE trained on synthetic client populations. The method achieves the same per‑round communication cost as FedAvg, allows absent clients to receive a personalized model with a single forward pass, and demonstrates superior performance across five heterogeneous benchmarks.

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arXiv Machine Learning
Oct 3

StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation

StoCFL is a clustered federated learning framework designed to address Non-IID data and dynamic client participation. It introduces a flexible clustering mechanism that allows arbitrary client participation and accommodates newly joined clients, improving data efficiency and model performance. Experiments on four Non-IID settings and a real-world dataset demonstrate that StoCFL achieves promising cluster results even when the number of clusters is unknown, outperforming baseline approaches across various scenarios.

By Dun Zeng, Xiangjing Hu, Shiyu Liu, Yue Yu, Qifan Wang, Zenglin Xu
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arXiv Machine Learning
1d ago

Evolving Federated Density Peak Clustering with Shuffler-Based Anonymization

The paper introduces Evolving Federated Density Peak Clustering (eFDPC), a one‑shot federated clustering method that merges evolving fuzzy data‑stream clustering with density peak clustering. Each client independently updates micro‑clusters from private mini‑batches, and only selected density peaks are sent to the server, reducing communication. A shuffler‑based anonymization layer protects client contributions, and two variants—eFDPC‑P and eFDPC‑G—are evaluated on synthetic and real datasets, outperforming recent federated clustering baselines in one‑shot settings and handling high‑dimensional, non‑IID data.

By Miha O\v{z}bot, Seiichi Ozawa, Igor \v{S}krjanc
benchmarks
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arXiv Machine Learning
Aug 31

Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning

The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.

By Yiming Xie, Lili Su, Ningfang Mi
fine-tuning
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arXiv Machine Learning
Jul 3

Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning

arXiv:2607. 01474v1 Announce Type: new Abstract: Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard centralized techniques due to privacy and heterogeneity constraints.

By Haemin Park, Diego Klabjan, Martin W. Braun, Xiuqi Li, Balakrishnan Ananthanarayanan
reinforcement-learningbenchmarkssafety
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