arXiv AI
CovFedAvg: Local Low-Loss Curves and Federated Model Selection
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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.
arXiv Machine Learning
Oct 8
ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learning
ORDERS is a personalized federated learning configuration that combines a shared backbone, private residual adapters, and a classifier, using geometric weights based on descending update norms, feature alignment, and private‑parameter perturbations. In extensive experiments on CIFAR‑10 and Sent140, ORDERS achieves slightly higher mean client accuracy than baseline methods, with a 5.47% reduction in parameter payload. Ablation studies show limited, dataset‑dependent benefits from norm ranking and alignment, and no clear advantage from perturbations.
arXiv Machine Learning
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Is High Local Accuracy Enough? Geometric Consensus with Support-Adaptive Statistical Personalization for Federated Learning
The paper introduces FedGRC, a federated learning framework that enhances both global recognition and personalization for heterogeneous clients. It combines Consensus‑Guided Representation Learning, Multilayer Metric Synthesis, and Support‑Adaptive Shrinkage Personalization to address failures on rare or unseen classes. Experiments on CIFAR‑10, CIFAR‑100, and the FedRS dataset show that FedGRC outperforms 21 baseline methods, improving global accuracy and personalization floors by over 2% in several settings.
arXiv Machine Learning
Sep 10
Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting
arXiv:2609.08554v1 Announce Type: new Abstract: In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. T...
arXiv Machine 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.
arXiv Machine Learning
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PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning
PACE introduces a propagation‑aware collaborative correction for one‑shot personalized federated graph learning. Each client sends a rank‑r update and a diagonal sketch of message moments, allowing the server to construct a correction that anchors to the receiver’s local model. A convex negative‑log‑likelihood calibration selects a single coefficient to blend local and external logits, improving accuracy and weighted‑F1 on most datasets while preserving local predictions when the correction is unhelpful.