Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to data-scarce cities without traffic data exchange, existing approaches in Federated Traffic Knowledge Transfer (FTT) still face several critical challenges such as potential privacy leakage, cross-city data distribution discrepancies, and low data quality, hindering their practical application in real-world scenarios. To this end, we present FedTT, a novel privacy-aware and efficient federated learning framework for cross-city traffic knowledge transfer. Specifically, our proposed framework includes four key innovations: (i) a traffic view imputation method for missing traffic data completion to enhance data quality, (ii) a traffic domain adapter for uniform traffic data transformation to address data distribution discrepancies, and (iii) a traffic secret aggregation protocol for secure traffic data aggregation to safeguard data privacy. Extensive experiments on 4 real-world datasets demonstrate that the proposed FedTT framework outperforms the 14 state-of-the-art baselines.
Python.version = 3.9.21
Other dependencies are listed in requirements.txt.
All the experiments are conducted in the federated environment on four nodes, one as a server and the other three as clients, each equipped with two Intel(R) Xeon(R) CPU E5-2650 [email protected] 12-core processors, 128GB of RAM, and a Internet speed of 100MB/s.
In this paper, we used 4 public datasets including PeMSD4, PeMSD8, FT-AED, HK-Traffic. The statistics of dataset as detailed below:
| Dataset | # instances | # sensors | Interval | City | Missing Rate |
|---|---|---|---|---|---|
| PeMSD4 | 16,992 | 307 | 5 min | San Francisco | 16.35% |
| PeMSD8 | 17,856 | 170 | 5 min | San Bernardino | 20.09% |
| FT-AED | 1,920 | 196 | 5 min | Nashville | 4.59% |
| HK-Traffic | 17,856 | 411 | 5 min | Hong Kong | 13.01% |
The running example of FedTT is as follows.
python main.py --address 'localhost' --batch_size 128 --clients_num 3 --device 'cuda' --epoch 1000 --frozen 5 --patience 100 --learning_rate 0.0005 --model 'mlp' --task 'flow' --test_split 0.1 --time_step 12 --train_split 0.8 --val_split 0.1