In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients' resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system's energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions.
翻译:近年来,移动客户的计算能力和存储能力大大提高,高效率地处理当地的一些应用。联邦学习是一个有希望的分布式机器学习解决方案,利用本地计算和地方数据培训人工智能模型。将本地计算和联合学习相结合,可以在确保本地数据隐私和充分利用移动客户资源的前提下,培训一个强大的AI模型。然而,当地数据的多样性,即非独立和相同的分配(非IID)和本地数据大小的不平衡,可能会给移动边缘计算系统中联邦化学习的应用带来瓶颈。受此启发,我们提议了一个基于集群的客户选择模式,该模式可以产生一种符合全球分布的联邦化虚拟数据集,以抵消数据异常性的影响,并证明拟议的计划可以趋于接近最佳解决方案。基于集群方法,我们提议在每个组内采用拍卖客户选择计划,以充分考虑系统的能源差异性,并在拟议的移动边缘计算系统(MEC)系统中采用纳什平衡型客户选择方案。我们提议采用的方法,可以更好地实现平衡消费和升级数据选择方法的趋同率。