Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing

Abstract--Recent advancements in large artificial intelligence models (LAMs) are driving significant innovations in mobile edge computing within next-generation wireless networks. However, the substantial demands for computational resources and large-scale training data required to train LAMs conflict with the limited storage and computational capacity of edge devices, posing significant challenges to training and deploying LAMs at the edge. In this work, we introduce the Networked Mixture-of-Experts (NMoE) system, in which clients infer collaboratively by distributing tasks to suitable neighbors based on their expertise and aggregate the returned results. For training the NMoE, we propose a federated learning framework that integrates both supervised and self-supervised learning to balance per-sonalization and generalization, while preserving communication efficiency and data privacy. We conduct extensive experiments to demonstrate the efficacy of the proposed NMoE system, providing insights and benchmarks for the NMoE training algorithms. The recent wave of progress in large artificial intelligence models (LAMs) has triggered a variety of novel technologies, such as large language models (LLMs), vision-language models (VLMs), and artificial intelligence (AI) agents [1], which present exciting opportunities for next-generation wireless communications.