Smart City Gnosys
Smart city article details
| Title | Fedgode: Secure Traffic Flow Prediction Based On Federated Learning And Graph Ordinary Differential Equation Networks |
|---|---|
| ID_Doc | 26393 |
| Authors | Al-Huthaifi R.; Li T.; Al-Huda Z.; Huang W.; Luo Z.; Xie P. |
| Year | 2024 |
| Published | Knowledge-Based Systems, 299 |
| DOI | http://dx.doi.org/10.1016/j.knosys.2024.112029 |
| 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. © 2024 Elsevier B.V. |
| Author Keywords | Federated learning; Intelligent transportation systems; Ordinary differential equation network; Security and privacy; Traffic flow prediction |
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