FedForce: Network-adaptive Federated Learning for Reinforced Mobility Prediction

Federated Learning (FL) is gaining popularity in trajectory prediction field due to its privacy-preserving and scalability capabilities. However, deploying FL on resource-constrained devices and varying wireless network conditions can be challenging. Moreover, the design of FL’s distributed neural architectures is complex requiring expert knowledge. To address these limitations, we propose the network-adaptive FEDerated learning for reinFORCEd mobility prediction (FedForce) system. FedForce uses reinforcement learning to design a transformer neural network that jointly optimizes prediction accuracy, training time, and transmission time based on the unique features of the mobility dataset, the client’s computing capacity, and the available network throughput. FedForce achieves an average displacement error of 0.20m on the ETH+UCY dataset and 76% accuracy on the Orange dataset (−0.02m and 10% better than the best-performing existing baselines, respectively), while reducing FL training and transmission time by a factor of 3. FedForce can save up to 80% of computational resources and 96% of communication overheads with negligible accuracy decrease.