Inter-Server Collaborative Federated Learning for Ultra-Dense Edge Computing

Abstract

Increasingly serious data security and privacy protection issues make federated learning (FL) gradually evolve to be an important technology in the field of artificial intelligence (AI). Meanwhile, in consideration of the huge demands for network access and computing resources from massive IoT devices, ultra-dense edge computing (UDEC), which integrates mobile edge computing (MEC) and ultra-dense network (UDN), has turned out to be a promising network architecture in the era of 5G and even 6G. Facing requirements on ultra-low processing latency, performing FL for UDEC confronts many challenges, one of which is how to relieve the barrel effect caused by the difference in computing power of local devices while ensuring overall FL efficiency. Nevertheless, little work can be found in this area. Toward this end, the paper takes the lead in studying FL for UDEC, and proposes an inter-server collaborative federated learning method by grouping the servers and clients. Theoretical analysis and numerical results corroborate that our proposed inter-server collaborative method can significantly reduce the waiting time during local training without reducing the learning accuracy, thus improving the overall efficiency.