arXiv Machine Learning
FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning
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Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
arXiv:2606. 01081v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy.
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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.
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FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift
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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.
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Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
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From Task Mixtures to Specialized Experts
The paper investigates federated learning where each client’s data consists of unknown mixtures of distinct tasks, a scenario termed compound heterogeneity. It shows that when tasks share a common feature geometry, the optimal model for a mixed client is a convex combination of task‑specific models, motivating input‑dependent routing to specialized experts. The authors propose FedSEE, a method that recovers task experts via a convex program and achieves better performance than baselines, reducing negative transfer by 2.9 points overall and 3.7 points for the worst‑served quartile.