A Federated Parameter Aggregation Method for Node Classification Tasks
  with Different Graph Network Structures
Hao Song Jiacheng Yao Zhengxi Li Shaocong Xu Shibo Jin Jiajun Zhou Chenbo Fu Qi Xuan Shanqing Yu
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.