Overcoming Catastrophic Forgetting in Federated Continual Graph Learning for Resource-Limited Mobile Devices

Abstract

Federated Graph Learning (FGL) enables multiple clients to collaboratively learn node representations from private subgraph data, such as user transactions or social networks. Local models are trained on clients and then aggregated by a central server, supporting large-scale graph learning without sharing raw data. However, most existing FGL methods assume that the number of nodes in the graph remains constant, while real-world scenarios often evolve, with new nodes and edges continually added and older ones removed due to limited device memory. We define this setting as Federated Continual Graph Learning (FCGL). In FCGL, global model aggregation may cause interference occur inter-task and inter-client, therefore, FCGL suffers from the global catastrophic forgetting, as the global model adapts to newly added nodes, it loses knowledge acquired from earlier graph data of clients. To address this, we propose GRE-FL, a generative replay framework, which can mitigate global catastrophic forgetting by generating a global summary graph at the server to preserve critical information from historical nodes. It also improves performance by equipping local models with a gating graph attention network for better feature extraction. Experiments show that GRE-FL achieves strong performance across multiple datasets.