Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
Federated learning (FL) enables collaborative model training across distributed clients while keeping data local. A central challenge is determining which client updates are beneficial for aggregation with respect to each client's target domain. Existing methods typically address this problem in parameter space by comparing model parameters or gradients. However, parameter-space similarity can be a poor proxy for predictive behavior, especially under heterogeneous, non-IID data. Consequently, updates that are misaligned with a client's target domain, including those caused by heterogeneous data or malfunctioning clients, may degrade local model performance. We propose Local Inference Guid...
| Authors | Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 13 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.