FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks

TL;DR

FedGAT is a novel federated learning algorithm that approximates Graph Attention Networks with minimal communication, enabling privacy-preserving semi-supervised node classification with performance close to centralized models.

Contribution

Introduces FedGAT, a federated approximation method for GATs that requires only one communication round, reducing overhead while maintaining high accuracy.

Findings

FedGAT achieves nearly the same accuracy as centralized GATs.

The algorithm is robust to the number of clients and data distribution.

It significantly reduces communication overhead in federated GAT training.

Abstract

Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of huge online marketplaces. However, privacy regulations often require locally generated data to be stored on local clients. The graph is then naturally partitioned across clients, with no client permitted access to information stored on another. Cross-client edges arise naturally in such cases and present an interesting challenge to federated training methods, as training a graph model at one client requires feature information of nodes on the other end of cross-client edges. Attempting to retain such edges often incurs significant communication overhead, and dropping them altogether reduces model performance. In simpler models such as Graph Convolutional Networks, this can be fixed by communicating a limited amount of feature information across clients before training, but GATs (Graph Attention Networks) require additional information that cannot be pre-communicated, as it changes from training round to round. We introduce the Federated Graph Attention Network (FedGAT) algorithm for semi-supervised node classification, which approximates the behavior of GATs with provable bounds on the approximation error. FedGAT requires only one pre-training communication round, significantly reducing the communication overhead for federated GAT training. We then analyze the error in the approximation and examine the communication overhead and computational complexity of the algorithm. Experiments show that FedGAT achieves nearly the same accuracy as a GAT model in a centralised setting, and its performance is robust to the number of clients as well as data distribution.