4 citations · 7 across the 17 of their papers we have counts for
4 papers · 1 filter
Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
Zifan Zhang, Minghong Fang, Dianwei Chen +5
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in…
Poisoning Attacks and Defenses to Federated Unlearning
Wenbin Wang, Qiwen Ma, Zifan Zhang +3
Federated learning allows multiple clients to collaboratively train a global model with the assistance of a server. However, its distributed nature makes it susceptible to poisonin…
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
Hairi, Minghong Fang, Zifan Zhang +2
In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the pre…
Byzantine-Robust Decentralized Federated Learning
Minghong Fang, Zifan Zhang, Hairi +5
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system foll…