FedDiG: Federated Continual Graph Learning with Disentangled Generative Replay

Federated Continual Graph Learning (FCGL) is essential for real-world applications where graph data is both decentralized across clients and evolving over time. The primary challenge in this setting is catastrophic forgetting without centralizing raw client graphs. This forgetting manifests in two coupled aspects: the erosion of semantic decision boundaries and the degradation of structural relational patterns. Graph representations entangle node-level semantics with topology-dependent message passing, so a shift in either aspect can silently corrupt the other. In the federated continual setting this coupling is amplified: raw graphs are not centralized, precluding a shared raw-graph replay buffer, while heterogeneous topologies fragment old knowledge. Existing methods generally do not explicitly disentangle semantic and structural factors at the representation level, limiting factor-specific retention under drift. We propose FedDiG (Federated Disentangled continual Graph learning), which mitigates forgetting through post-hoc dual-gradient feature masking and generative replay. FedDiG derives complementary semantic and structural channels, trains a client-side generator on the current local graph, and applies server-side distillation that aligns both channels. Extensive experiments on seven datasets show that FedDiG achieves the highest average accuracy and lowest average forgetting among the compared baselines.