Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution, size, and device capabilities, poses challenges in user federation. Recognizing this, Personalized Federated Learning (PFL) emphasizes tailoring learning processes to individual data profiles. In this paper, we address the complexity of clustering users in PFL, especially in dynamic networks, by introducing a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach. The dUCB algorithm ensures that new users can effectively find the best cluster for their data distribution by balancing exploration and exploitation. The performance of our algorithm is evaluated in various cases, showing its effectiveness in handling dynamic federated learning scenarios.

翻译：联邦学习（FL）提供了一种去中心化的机器学习模型训练方法，优先考虑数据隐私。然而，FL网络中固有的异构性——源于数据分布、数据规模及设备能力的差异——给用户联合带来了挑战。为此，个性化联邦学习（PFL）强调根据个体数据特征定制学习过程。本文针对PFL中用户聚类的复杂性，尤其是在动态网络环境下，引入了一种受多臂老虎机（MAB）方法启发的动态上置信界（dUCB）算法。dUCB算法通过平衡探索与利用，确保新用户能高效找到最适合其数据分布的聚类。我们在多种场景下评估了该算法的性能，结果表明其在处理动态联邦学习场景中的有效性。