RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
This work addresses privacy-preserving collaborative intelligence for vehicular applications, representing an incremental improvement in domain-specific federated learning.
The paper tackles the challenge of adapting federated learning to vehicular crowd intelligence by addressing issues like unreliable centralized aggregation and non-IID data, resulting in a framework that outperforms baselines in communication overhead, model accuracy, and convergence.
Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial. First, the centralized model aggregation is unreliable in VCI because of the existence of stragglers with unfavorable channel conditions. Second, existing FL schemes are vulnerable to Non-IID data, which is intensified by the data heterogeneity in VCI. This paper proposes a novel federated learning framework called RaftFed to facilitate privacy-preserving VCI. The experimental results show that RaftFed performs better than baselines regarding communication overhead, model accuracy, and model convergence.