Federated graph learning with spatio-temporal dynamics for cross-border recommendation.

Hierarchical Graph Federated Learning for Cross-Domain Intelligent Transportation Networks

Intelligent transportation systems (ITS) generate vast heterogeneous data from roadside units (RSUs), traffic management centers (TMCs), and vehicular networks, posing challenges for privacy, scalability, and cross-domain learning. This paper proposes a Hierarchical Graph Federated Learning (HG-FL) framework that integrates multi-level aggregation, spatio-temporal graph modeling, and autoencoder-based feature compression to enable privacy-preserving, distributed ITS analytics. The framework mirrors real-world ITS hierarchy through three tiers: local RSU-level training, regional TMC-level aggregation, and global cross-domain coordination. Experiments conducted on the CIC-IoV 2024 Decimal Dataset and the NF-ToN-IoT-v2 dataset demonstrate that HG-FL achieves superior performance compared to centralized, flat federated, and graph-only baselines. Specifically, on the combined dataset, HG-FL attains an AUROC of 0.964, AUPRC of 0.947, and F1-score of 0.929, while reducing communication cost to 162 MB and convergence rounds to 15. For SLA violation risk prediction, it achieves an RMSE of 0.121 and a Brier score of 0.059, outperforming baseline methods. These results highlight the framework’s scalability, robustness, and effectiveness in achieving cross-domain generalization and service-level assurance within privacy-preserving ITS environments.

FedIGL: Federated Invariant Graph Learning for Non-IID Graphs

Federated Graph Learning (FGL) effectively facilitates cross-domain graph model training by enabling decentralized learning across multiple domains, while ensuring data privacy through local data storage and communication of model updates instead of raw data. Existing approaches usually assume shared generic knowledge (e.g., prototypes, spectral features) via aggregating local structures statistically to alleviate structural heterogeneity. However, imposing overly strict assumptions about the presumed correlation between structural features and the global objective often fails in generalizing to local tasks, leading to suboptimal performance. To tackle this issue, we propose a Fed erated I nvariant G raph L earning ( FedIGL ) framework based on invariant learning, which effectively disrupts spurious correlations and further mines the invariant factors across different distributions. Specifically, a server-side global model is trained to capture client-agnostic subgraph patterns shared across clients, whereas client-side models specialize in client-specific sub-graph patterns. Subsequently, without compromising privacy, we propose a novel Bi-Gradient Regularization strategy that introduces gradient constraints to guide the model in identifying client-agnostic and client-specific subgraph patterns for better graph representations. Extensive experiments on graph-level clustering and classification tasks demonstrate the superiority of FedIGL against its competitors.

Personalized federated learning based on self-excluding aggregation with knowledge distillation

FedTT: A Federated Spatio-Temporal Learning Framework for Cross-City Traffic Knowledge Transfer

Traffic prediction aims to forecast future traffic conditions from historical spatio-temporal data. In practice, many cities face limited traffic data due to incomplete sensing infrastructures, making accurate prediction difficult. Federated traffic knowledge transfer offers a promising solution by allowing data-rich source cities to assist data-scarce target cities while keeping local data private. However, existing methods still face key challenges, including cross-city distribution discrepancies, incomplete or noisy observations, and potential privacy leakage from shared gradients or model parameters during federated optimization. To this end, we propose FedTT, a federated spatio-temporal learning framework for cross-city traffic knowledge transfer. FedTT features three co-designed components: (i) a Traffic Domain Adapter (TDA) that explicitly aligns heterogeneous traffic data distributions across cities to improve transfer effectiveness, (ii) a Traffic View Imputation (TVI) module that enhances data quality by completing missing traffic observations through spatio-temporal dependency modeling, and (iii) a lightweight Traffic Secret Aggregation (TSA) protocol that enables attack-resistant knowledge aggregation with formal privacy analysis, without relying on heavy cryptographic primitives. Extensive experiments on four real-world traffic datasets demonstrate that FedTT consistently outperforms 18 state-of-the-art baselines, achieving improved prediction accuracy while maintaining strong empirical resistance to attacks.

Federated Learning Approach for Multi-Regional Traffic Flow Prediction

Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.

FEDGNNREC: A FEDERATED GRAPH NEURAL NETWORK FRAMEWORK FOR PRIVACY-PRESERVING RECOMMENDATIONS

Recommender systems are common in today's online platforms to give personalised recommendations RS depending on the interaction of the users. Nevertheless, the conventional centralised recommendation methods demand gathering user information in central servers, which poses a serious privacy risk and a higher risk of information leakage. Concurrently, most of the traditional models do not reflect the structural relations that exist between the user and item interactions. In order to resolve these problems, this paper will suggest a FGNN framework, in which privacy-preserving recommendation is provided. The method proposed is a federated learning, which is merged with GNN to allow decentralised training whilst maintaining the relational structure of data in terms of interaction. All the clients build a local user-item graph and learn GNN-based embedding without distributing raw data. The aggregating server receives updates on the model by a federated averaging mechanism in order to construct a global model. The experimental results with Amazon review data indicate that the proposed federated GNN has a correlation of 0.9176, RMSE of 0.4021, and MAE of 0.2135, which is better than multiple centralised and base methods. Full privacy is also guaranteed by the model since the user data is not transferred to any other clients. These findings demonstrate the usefulness of federated graph-based learning in order to have secure and accurate recommendation systems.