Utilizing the federated learning (FL) technique, data owners can collaboratively train artificial intelligence models, retaining all training data on their premises to minimize the potential for personal data breaches. However, self-interested users (e.g., free riders) bring new challenges that hinder the development of FL techniques. To this end, we propose a two-stage client selection scheme comprising a multiarmed bandit (MAB)-based candidate client selection method and an auction-based training client selection method. Specifically, our client selection scheme initially formulates the FL system into an MAB system, where clients are the arms and the server is the player. Then, we quantify the similarity between a local model and the server side, which is the designed metric for model aggregation and reward computation updating based on the fuzzy mathematical strategy. Next, based on the Thompson Sampling strategy, the server can intelligently determine the reward of each client, and clients with more significant rewards have the chance for local model training. With an auction method, the server can determine the training clients to reduce the training cost while maximizing each client’s revenue. Extensive experiments on real-world data sets demonstrate that the proposed scheme outperforms representative FL schemes (i.e., FedAvg, FedProx, FedMax, and MFL) regarding the model’s convergence rate and cost in FL systems with free riders.

中文翻译：

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针对搭便车攻击的联邦学习的两阶段客户端选择：一种多臂老虎机和基于拍卖的方法


利用联邦学习 （FL） 技术，数据所有者可以协作训练人工智能模型，将所有训练数据保留在其本地，以最大限度地减少个人数据泄露的可能性。然而，自私自利的用户（例如，搭便车者）带来了阻碍 FL 技术发展的新挑战。为此，我们提出了一种两阶段的客户选择方案，包括基于多臂老虎机 （MAB） 的候选客户选择方法和基于拍卖的培训客户选择方法。具体来说，我们的客户端选择方案最初将联邦学习系统构建为 MAB 系统，其中客户端是手臂，服务器是播放器。然后，我们量化了本地模型与服务器端的相似性，这是基于模糊数学策略的模型聚合和奖励计算更新的设计指标。接下来，基于 Thompson Sampling 策略，服务器可以智能地判断每个客户端的奖励，奖励更显著的客户端有机会进行本地模型训练。通过竞价方式，服务端可以确定训练客户端，降低训练成本，同时最大化每个客户端的收入。对真实数据集的广泛实验表明，在搭便车的 FL 系统中，所提出的方案在模型的收敛速率和成本方面优于代表性的 FL 方案（即 FedAvg、FedProx、FedMax 和 MFL）。