Multi-Task Federated Learning for Traffic Prediction and Its Application to Route Planning


A novel multi-task federated learning (FL) framework is proposed in this paper to optimize the traffic prediction models without sharing the collected data among traffic stations. In particular, a divisive hierarchical clustering is first introduced to partition the collected traffic data at each station into different clusters. The FL is then implemented to collaboratively train the learning model for each cluster of local data distributed across the stations. Using the multi-task FL framework, the route planning is studied where the road map is modeled as a time-dependent graph and a modified A* algorithm is used to determine the route with the shortest traveling time. Simulation results showcase the prediction accuracy improvement of the proposed multi-task FL framework over two baseline schemes. The simulation results also show that, when using the multi-task FL framework in the route planning, an accurate traveling time can be estimated and an effective route can be selected.


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