论文标题
小组个性化联合学习
Group Personalized Federated Learning
论文作者
论文摘要
联合学习（FL）可以通过在客户的物理设备上以偏心方式培训共享模型来帮助促进数据隐私。在当地数据的高度异质分布的存在下，个性化的FL策略旨在减轻潜在的客户漂移。在本文中，我们介绍了针对FL的应用的组个性化方法，在客户之间存在固有的分区，这些分区存在很大的不同。在我们的方法中，通过在每个均质客户群体上的另一个FL培训过程中微调了全球FL模型，此后，每个客户群都会进一步调整每个小组的FL模型。提出的方法可以从贝叶斯分层建模的角度很好地解释。通过在两个现实世界数据集上的实验，我们证明这种方法可以比其他FL对应物获得卓越的个性化表现。
Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of highly heterogeneous distributions of local data, personalized FL strategy seeks to mitigate the potential client drift. In this paper, we present the group personalization approach for applications of FL in which there exist inherent partitions among clients that are significantly distinct. In our method, the global FL model is fine-tuned through another FL training process over each homogeneous group of clients, after which each group-specific FL model is further adapted and personalized for any client. The proposed method can be well interpreted from a Bayesian hierarchical modeling perspective. With experiments on two real-world datasets, we demonstrate this approach can achieve superior personalization performance than other FL counterparts.
