17 citations · 83 across the 14 of their papers we have counts for
14 papers
UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang +2
Personalized federated learning has gained significant attention as a promising approach to address the challenge of data heterogeneity. In this paper, we address a relatively unex…
You Can Backdoor Personalized Federated Learning
Tiandi Ye, Cen Chen, Yinggui Wang +2
Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A…
SLPerf: a Unified Framework for Benchmarking Split Learning
Tianchen Zhou, Zhanyi Hu, Bingzhe Wu +1
Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks. To address that…
Backdoor Defense with Machine Unlearning
Yang Liu, Mingyuan Fan, Cen Chen +4
Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper,…
Practical and Light-weight Secure Aggregation for Federated Submodel Learning
Jamie Cui, Cen Chen, Tiandi Ye +1
Recently, Niu, et. al. introduced a new variant of Federated Learning (FL), called Federated Submodel Learning (FSL). Different from traditional FL, each client locally trains the…
Privacy Threats Analysis to Secure Federated Learning
Yuchen Li, Yifan Bao, Liyao Xiang +4
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never le…