A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
This addresses privacy-preserving collaborative training for graph data in federated settings, but it is incremental as it adapts existing federated methods to graph-specific challenges.
The paper tackles the problem of federated learning for graph neural networks when clients have different graph structures, proposing a method called FLGNN that aggregates parameters across layers and shows robustness with reduced privacy theft success rates through differential privacy defenses.
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of graph neural networks, the nodes and network structures of graphs held by clients are different in many practical applications, and the aggregation method that directly shares model gradients cannot be directly applied to this scenario. Therefore, this work proposes a federated aggregation method FLGNN applied to various graph federation scenarios and investigates the aggregation effect of parameter sharing at each layer of the graph neural network model. The effectiveness of the federated aggregation method FLGNN is verified by experiments on real datasets. Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments. The results show that FLGNN performs good robustness, and the success rate of privacy theft is further reduced by adding differential privacy defense methods.