454 citations · 3.1k across the 117 of their papers we have counts for
4 papers · 1 filter
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
Decoupling the Class Label and the Target Concept in Machine Unlearning
Jianing Zhu, Bo Han, Jiangchao Yao +3
Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previ…
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Ziqing Fan, Shengchao Hu, Jiangchao Yao +4
In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resul…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…