FedCross: Intertemporal Federated Learning Under Evolutionary Games
Jianfeng Lu, Ying Zhang, Riheng Jia, Shuqin Cao, Jing Liu, Hao Fu
TL;DR
FedCross tackles intertemporal continuity in federated learning over highly mobile networks by coupling cross-region task migration with an incentive mechanism. It models user-region decisions through evolutionary game dynamics to forecast mobility and stabilize participation, while a greedy procurement auction reallocates rewards to base stations providing high-quality updates. The framework combines a multi-objective migration strategy with channel-aware communication and privacy-preserving gradient compression, achieving reduced communication overhead and sustained FL performance. Theoretical analysis establishes stability and incentive compatibility, and experiments on geospatially augmented MNIST and CIFAR-10 datasets demonstrate practical gains in task continuity, model accuracy, and participant engagement in dynamic edge environments.
Abstract
Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Overcoming this obstacle involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose an intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. Specifically, FedCross comprises two distinct stages. In Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead.