Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks

Federated learning (FL) is a distributed machine learning (ML) framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data. In FL, the limited number of participants for model aggregation and communication latency are two major bottlenecks. Hierarchical federated learning (HFL), with a cloud-edge-client hierarchy, can lever-age the large coverage of cloud servers and the low transmission latency of edge servers. There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles. However, the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training. In this context, HFL, which stands out for lower latency, wider coverage and more participants, is promising in vehicular networks. In this paper, we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks. Then, the architecture of HFL is illustrated. Next, we clarify new issues in HFL and review several existing solutions. Furthermore, we introduce some typical use cases in vehicular networks as well as our initial ef-forts on implementing HFL in vehicular networks. Finally, we conclude with future research directions.