4 papers
Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits
Tianyi Xu, Jiaxin Liu, Nicholas Mattei +1
We propose a multi-agent multi-armed bandit (MA-MAB) framework aimed at ensuring fair outcomes across agents while maximizing overall system performance. A key challenge in this se…
A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report)
Henger Li, Tianyi Xu, Tao Li +3
Recent research efforts indicate that federated learning (FL) systems are vulnerable to a variety of security breaches. While numerous defense strategies have been suggested, they…
Online Learning with Probing for Sequential User-Centric Selection
Tianyi Xu, Yiting Chen, Henger Li +3
We formalize sequential decision-making with information acquisition as the probing-augmented user-centric selection (PUCS) framework, where a learner first probes a subset of arms…
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks
Tao Li, Henger Li, Yunian Pan +3
Federated learning (FL) is susceptible to a range of security threats. Although various defense mechanisms have been proposed, they are typically non-adaptive and tailored to speci…