Matching Theory Based Low-Latency Scheme for Multi-Task Federated Learning in MEC Networks

Nowadays there is an ever-increasing interests in federated learning, which allows end devices to collaboratively train a global machine learning model in a decentralized paradigm without sharing individual data. Despite the advantages of low communication cost and preserving data privacy, federated learning is also facing with new challenges to address. Practically, end devices will consider the resources cost and willingness caused by machine learning model training when they are invited to participant a federated learning task. So how to assign the preferable tasks to the devices with high willingness has to be considered. Besides, the end devices have the property of high mobility, which means the time of devices localizing within the network is limited. Therefore, to reduce the task execution time is necessary. To address these problems, we first analyze and formulate the latency minimization problem for multi-task federated learning in a multi-access edge computing (MEC) network scenario. Then we model the corresponding problem as a matching game to find the optimal task assignment solutions. Moreover, considering the large scale Internet of Things (IoTs) scenario, it is almost impossible for two sides to know the details of every individual of the other side so that the complete preference list cannot be built in reality. Therefore, we propose an algorithm for large scale matching with the incomplete preference list to address the problem. Finally, we conduct the numerical simulation in various cases to demonstrate the effectiveness of our proposed method. The results show that our approach can achieve similar performance with the complete preference list case.