Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting, impairing performance on earlier tasks.Despite existing continual graph learning (CGL) methods mitigating this to some extent, they rely on centralized architectures and ignore the potential of distributed graph databases to leverage collective intelligence.To this end, we propose Federated Continual Graph Learning (FCGL) to adapt GNNs across multiple evolving graphs under storage and privacy constraints.Our empirical study highlights two core challenges: local graph forgetting (LGF), where clients lose prior knowledge when adapting to new tasks, and global expertise conflict (GEC), where the global GNN exhibits sub-optimal performance in both adapting to new tasks and retaining old ones, arising from inconsistent client expertise during server-side parameter aggregation.To address these, we introduce POWER, a framework that preserves experience nodes with maximum local-global coverage locally to mitigate LGF, and leverages pseudo-prototype reconstruction with trajectory-aware knowledge transfer to resolve GEC.Experiments on various graph datasets demonstrate POWER's superiority over federated adaptations of CGL baselines and vision-centric federated continual learning approaches.

The data stream generated by users on web applications is often collected using a local differential privacy (LDP) approach to ensure privacy. This approach offers rigorous theoretical guarantees and low computational overhead, albeit at the expense of data utility. Data utility encompasses both the value of individual data points and the temporal relevance that exists between them, but existing studies primarily focus on enhancing the former utility while neglecting the latter. Furthermore, the collected data often requires cleaning, and we have demonstrated through a case study that data stream lacking time relevance poses a significant risk to users' privacy during the cleaning process. In this paper, for the first time we present an online LDP publishing mechanism while preserving the inherent temporal relevance for the infinite stream, called the Sampling Period Perturbation Algorithm (SPPA). Specifically, we model the temporal relevance between data points as the Fourier interpolation function, resulting in a computational complexity reduction from O(n2) to O(n log n) when compared with the conventional Markov approach in the offline setting. To strike a better balance between privacy and utility, we add noise to the sampling period due to its minimal impact on sensitivity, which is analyzed by our novel concepts of (ε,τ)-temporal indistinguishability and (ε,w,τ)-event LDP. Through extensive experiments, SPPA exhibits superior performance in terms of both data utility and privacy preservation compared to the state-of-the-art baselines. In particular, when ε=1, compared with the state-of-the-art baseline, SPPA diminishes the MSE by up to 64.2%, and raises the event monitoring efficiency by up to 21.4%.

Federated graph learning (FGL) has risen as a promising paradigm for collaboratively training graph neural networks while safeguarding data privacy. Nevertheless, the distributed nature of FGL also renders it susceptible to backdoor attacks. Although backdoor attacks are recognized as a significant threat to both centralized graph learning and federated learning (FL), the study of such attacks in FGL remains very limited. Current research on FGL backdoor attacks often merely adapts centralized graph backdoor attacks or FL backdoor attacks designed for image classification tasks to the FGL context, leaving key issues such as the effectiveness of triggers and the stealthiness of malicious models largely unexplored. To bridge this research gap, in this paper, we propose a novel backdoor attack, named FGLBA, targeting the FGL paradigm. Specifically, we design an input-aware trigger generator that generates a customized trigger for each target node based on its feature vector and neighborhood information, making that poisoned nodes injected with triggers are more likely misclassified into the category specified by the attacker. Additionally, we develop a stealthy federated backdoor training strategy that leverages collaborative optimization among multiple malicious clients to circumvent existing server-side defenses. The trigger generator and malicious clients' local models are iteratively optimized through a bilevel optimization framework, enabling the malicious models to achieve optimal attack performance under the optimal trigger generator. Extensive experiments on 4 real-world datasets demonstrate the effectiveness and superiority of our attack, outperforming all baseline attacks and successfully bypass 6 state-of-the-art and classical FL backdoor defenses.