MemEvo-FCGL: Structural Memory Evolution for Federated Continual Graph Learning

Federated continual graph learning (FCGL) enables distributed clients to collaboratively learn from sequential graph-task views without sharing local observations. However, clients must preserve previous knowledge while adapting to new graph patterns, and the server must coordinate structurally heterogeneous clients. Existing methods often describe task evolution using coarse task- or label-level statistics and preserve historical knowledge through raw-sample replay, limiting their ability to capture structural changes. We propose MemEvo-FCGL, a structural memory evolution framework for local knowledge retention and cross-client coordination. Each client learns memory keys that distill node representations into a compact state describing the current task's structural patterns. The server compares current states along client memory-state trajectories to adapt model aggregation and prototype-based knowledge transfer, reducing interference from structurally incompatible updates. Meanwhile, a bounded abstract key-value memory merges similar historical patterns, dynamically reorganizes novel patterns under a fixed capacity, and retrieves relevant knowledge through attention. Across six protocol-matched node-classification benchmarks, MemEvo-FCGL attains the highest final average performance on five while retaining a bounded number of abstract memory slots. These results demonstrate the effectiveness of structural memory evolution for continual learning over decentralized class-incremental graph streams.The code is available at https://anonymous.4open.science/r/anonymous_accept.