Summary of Detrigger: a Gradient-centric Approach to Backdoor Attack Mitigation in Federated Learning, by Kichang Lee et al.
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learningby Kichang Lee, Yujin Shin,…
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learningby Kichang Lee, Yujin Shin,…
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanismby Yasaman Saadati, M. Hadi AminiFirst submitted…
Federated Contrastive Learning of Graph-Level Representationsby Xiang Li, Gagan Agrawal, Rajiv Ramnath, Ruoming JinFirst submitted…
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learningby Zhenyu Wen, Wanglei Feng, Di Wu,…
A Potential Game Perspective in Federated Learningby Kang Liu, Ziqi Wang, Enrique ZuazuaFirst submitted to…
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Methodby Yu Jiang, Chee Wei Tan, Kwok-Yan…
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transportby Sannara Ek, Kaile Wang, François…
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachby Ratun Rahman, Neeraj Kumar,…
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solutionby Jinbo…
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring Pluginby Youngjoon Lee,…