4 papers
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
Xingyu Lyu, Ning Wang, Yang Xiao +4
Federated Learning (FL) is a popular paradigm enabling clients to jointly train a global model without sharing raw data. However, FL is known to be vulnerable towards backdoor atta…
Demystifying Private Transactions and Their Impact in PoW and PoS Ethereum
Xingyu Lyu, Mengya Zhang, Xiaokuan Zhang +3
In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the c…
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks
Xingyu Lyu, Ning Wang, Yang Xiao +4
Federated Learning is a popular paradigm that enables remote clients to jointly train a global model without sharing their raw data. However, FL has been shown to be vulnerable tow…
SSE-SAM: Balancing Head and Tail Classes Gradually through Stage-Wise SAM
Xingyu Lyu, Qianqian Xu, Zhiyong Yang +2
Real-world datasets often exhibit a long-tailed distribution, where vast majority of classes known as tail classes have only few samples. Traditional methods tend to overfit on the…