arXiv AI
Jun 30
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.
FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning
FedFIbOS introduces a Fisher‑importance based criterion for selecting submodel parameters in heterogeneous federated learning, addressing the lack of theoretical justification in prior heuristic methods. By deriving a Fisher‑weighted quadratic masking surrogate and showing that the raw Fisher top‑k rule satisfies this surrogate under a Fisher‑dominant ranking condition, the method preserves convergence guarantees while efficiently estimating Fisher scores from squared gradients. Experiments on CIFAR‑10, CIFAR‑100, and AGNews demonstrate that FedFIbOS outperforms state‑of‑the‑art approaches by roughly 10% in accuracy, especially under strong non‑IID heterogeneity.
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FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
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arXiv Machine Learning
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arXiv Machine Learning
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Personalized Additive Modeling for Multi-level Federated Learning
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arXiv Machine Learning
Aug 12
Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension
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arXiv Machine Learning
Jul 7
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
arXiv:2607. 02681v1 Announce Type: cross Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging.
arXiv Machine Learning
Aug 31
Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning
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arXiv Statistics ML
Sep 10
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
arXiv:1907.06994v2 Announce Type: replace-cross Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...
arXiv Machine Learning
Aug 11
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
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arXiv Machine Learning
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Direct Fisher Score Estimation for Likelihood Maximization
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arXiv Machine Learning
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arXiv Machine Learning
Aug 13
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.