Federated graph neural networks in non-IID scenarios—A comprehensive survey

Abstract

Federated Graph Neural Networks (FedGNNs) have emerged as a promising solution to securely train structured graph data in Federated learning (FL) settings. In this paper, we present one of the first works that categorizes the various non-IID (Non-Identically and Independently Distributed) scenarios and challenges occurring in FedGNNs, offering insights into horizontal and vertical non-IID cases. Horizontal non-IID refers to variations in data distributions among clients, while vertical non-IID involves attribute and label disparities within the clients. We briefly discuss works addressing these scenarios and their respective advantages and disadvantages. Additionally, we explore other approaches like centralized and decentralized methods, in mitigating non-IID effects, highlighting their benefits in terms of shared knowledge, privacy preservation, and scalability. Furthermore, we emphasize the importance of evaluating and quantifying non-IIDness in graph data through statistical measures. Our work contributes to the understanding of FedGNNs’ applicability in healthcare, finance, recommender systems, transportation, and other domains. We also identify future research directions, such as taxonomy development, handling complete structural heterogeneity, and exploring adaptive mechanisms, to enhance the robustness and reliability of FedGNNs in real-time scenarios.