Rethinking Personalized Federated Learning with Clustering-based Dynamic
  Graph Propagation
Jiaqi Wang Yuzhong Chen Yuhang Wu Mahashweta Das Hao Yang Fenglong Ma
Most existing personalized federated learning approaches are based on
intricate designs, which often require complex implementation and tuning. In
order to address this limitation, we propose a simple yet effective
personalized federated learning framework. Specifically, during each
communication round, we group clients into multiple clusters based on their
model training status and data distribution on the server side. We then
consider each cluster center as a node equipped with model parameters and
construct a graph that connects these nodes using weighted edges. Additionally,
we update the model parameters at each node by propagating information across
the entire graph. Subsequently, we design a precise personalized model
distribution strategy to allow clients to obtain the most suitable model from
the server side. We conduct experiments on three image benchmark datasets and
create synthetic structured datasets with three types of typologies.
Experimental results demonstrate the effectiveness of the proposed work.