Clustered Graph Federated Personalized Learning
François Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh · 2022
This paper proposes a graph federated learning approach wherein multiple servers collaborate to enhance personalized learning over clustered clients, essentially performing correlated learning tasks. In contrast to earlier approaches, relying on a cluster-dedicated server topology, the proposed graph federated multitask learning (GFedMt) framework adopts a more general setting wherein clients of the same cluster are distributed across servers. In order to address problems with unbalanced client distributions among servers and clusters as well as data shortage of isolated clients, servers perform intra-cluster and inter-cluster learning collaboratively through local interaction with neighboring servers. Clients use the alternating direction method of multipliers (ADMM) to learn their local models. Numerical simulations demonstrate the ability of the proposed method to ensure fast and accurate convergence when data is scarce.