FedCross: Intertemporal Federated Learning Under Evolutionary Games

TL;DR

FedCross introduces an intertemporal federated learning framework that uses evolutionary game theory and auction mechanisms to reduce communication overhead and improve task continuity in mobile networks with high user mobility.

Contribution

It proposes a novel intertemporal incentive framework combining migration algorithms, evolutionary game theory, and auction mechanisms to enhance federated learning in dynamic mobile environments.

Findings

Significantly reduces communication overhead in federated learning.

Ensures task continuity despite user mobility and resource constraints.

Validates theoretical models with experimental results.

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.