Graph Federated Learning Based on the Decentralized Framework
Peilin Liu Yanni Tang Mingyue Zhang Wu Chen
Graph learning has a wide range of applications in many scenarios, which
require more need for data privacy. Federated learning is an emerging
distributed machine learning approach that leverages data from individual
devices or data centers to improve the accuracy and generalization of the
model, while also protecting the privacy of user data. Graph-federated learning
is mainly based on the classical federated learning framework i.e., the
Client-Server framework. However, the Client-Server framework faces problems
such as a single point of failure of the central server and poor scalability of
network topology. First, we introduce the decentralized framework to
graph-federated learning. Second, determine the confidence among nodes based on
the similarity of data among nodes, subsequently, the gradient information is
then aggregated by linear weighting based on confidence. Finally, the proposed
method is compared with FedAvg, Fedprox, GCFL, and GCFL+ to verify the
effectiveness of the proposed method. Experiments demonstrate that the proposed
method outperforms other methods.