FedGODE: Secure traffic flow prediction based on federated learning and graph ordinary differential equation networks

Rasha Al-Huthaifi, Tianrui Li, Zaid Al-Huda, Wei Huang, Zhipeng Luo, Peng Xie

Abstract: Traffic flow prediction (TFP) plays a key role in optimizing intelligent transportation systems and reducing congestion in smart cities. However, current centralized TFP systems suffer from several limitations: limited adaptability to localized traffic patterns, privacy concerns associated with sharing raw data, inability to provide real-time predictions, network inefficiency due to large data transmissions, and difficulties in handling heterogeneous traffic environments. FedGODE (Federated Graph Ordinary Differential Equations) model offers a comprehensive solution to these issues. By employing federated learning (FL), FedGODE enables Roadside-Units (local clients) to contribute to the model training process, addressing challenges of localized traffic patterns and ensuring a more accurate representation of diverse scenarios. FedGODE incorporates privacy-preserving mechanisms (local differential privacy, homomorphic encryption, and secure aggregation), mitigating privacy concerns. FedGODE facilitates real-time updates to the global model, ensuring adaptability to dynamic traffic conditions while reducing network inefficiencies by transmitting only model updates. FedGODE utilizes (average/median) aggregation methods to aggregate the client’s local updates. The performance of FedGODE was evaluated using six real-world traffic flow datasets and compared against nine baselines (ARIMA, VAR, FC-LSTM, STGCN, DCRNN, ASTGCN, Graph WaveNet, STG2seq, STGODE, and FedGRU). The results showed that FedGODE outperforms all baselines for short-and-long-term prediction. This approach makes FedGODE an effective solution for TFP scenarios, particularly in environments with heterogeneous traffic dynamics and privacy considerations.