Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collaborating with clients who have diverse data distributions can harm local model performance, especially with limited training data. To address this issue, we propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions and mitigate the data scarcity issue. It prioritizes and allocates resources based on data similarity. We further establish the theoretical convergence behavior of FedACS. Experiments on CIFAR10 and FMNIST validate FedACS's superiority, showcasing its potential to advance personalized federated learning. By tackling non-IID data challenges and data scarcity, FedACS offers promising advances in the field of personalized federated learning.

翻译：个性化联邦学习（PFL）依赖集体数据知识来构建定制化模型。然而，客户端之间的非独立同分布（non-IID）数据带来了重大挑战，因为与数据分布多样的客户端协作会损害本地模型性能，尤其是在训练数据有限的情况下。为解决这一问题，我们提出FedACS——一种带有注意力机制客户端选择的新颖PFL算法。FedACS集成了注意力机制，以增强数据分布相似客户端之间的协作，并缓解数据稀缺问题。它基于数据相似性进行资源优先级分配与调度。我们进一步建立了FedACS的理论收敛性行为。在CIFAR10和FMNIST上的实验验证了FedACS的优越性，展示了其推动个性化联邦学习发展的潜力。通过应对non-IID数据挑战与数据稀缺问题，FedACS为个性化联邦学习领域带来了有前景的进展。