Using a graph to represent the team behavior ensures that the relationship between Multi-Armed Bandits (MABs) are a class of reinforcement the agents are held. However, existing works either do learning problems where an agent is presented with a set of not consider the weight of each relationship (graph edges) arms (i.e., actions), with each arm giving a reward drawn (Madhushani and Leonard, 2020; Agarwal et al., 2021) or from a probability distribution unknown to the agent expect the user to manually set those weights (Moradipari (Lattimore and Szepesvári, 2020). The goal of the agent et al., 2022). is to maximize its total reward which requires balancing In this paper, we propose a new approach that combines exploration and exploitation. MABs offer a simple model graph optimization and MAMAB algorithms to enhance to simulate decision-making under uncertainty. Practical team performance by expediting the convergence to consensus applications of MAB algorithms include news recommendations of arm means. Our proposed approach: (Yang and Toni, 2018), online ad placement (Aramayo et al., 2022), dynamic pricing (Babaioff et al., 2015), improves team performance by optimizing the edge and adaptive experimental design (Rafferty et al., 2019). In weights in the graph representing the team structure contrast to single-agent cases, in certain applications such in large constrained teams, as search and rescue, a team of agents should cooperate does not require manual tuning of the graph weights, with each other to accomplish goals by maximizing team is independent of the MAMAB algorithm and only performance. Such problems are solved using Multi-Agent depends on the consensus formula, and Multi-Armed Bandit (MAMAB) algorithms (Xu et al., formulates the problem as a convex optimization, which 2020). Most existing algorithms rely on the presence of is computationally efficient for large teams.

Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning. By optimizing local models with knowledge distillation, FD circumvents the necessity of uploading large-scale model parameters to the central server, simultaneously preserving the raw data on local clients. Despite the growing popularity of FD, there is a noticeable gap in previous works concerning the exploration of poisoning attacks within this framework. This can lead to a scant understanding of the vulnerabilities to potential adversarial actions. To this end, we introduce FDLA, a poisoning attack method tailored for FD. FDLA manipulates logit communications in FD, aiming to significantly degrade model performance on clients through misleading the discrimination of private samples. Through extensive simulation experiments across a variety of datasets, attack scenarios, and FD configurations, we demonstrate that LPA effectively compromises client model accuracy, outperforming established baseline algorithms in this regard. Our findings underscore the critical need for robust defense mechanisms in FD settings to mitigate such adversarial threats.