AI-empowered mobile edge computing: inducing balanced federated learning strategy over edge for balanced data and optimized computation cost

Abstract

Abstract In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing access rights, privacy, security, and the utilization of heterogeneous data sources over mobile edge computing. Edge devices generate and gather data, across the network, in non-IID (independent and identically distributed) manner leading to potential variations in the number of data samples among these edge networks. A method is proposed to work in federated learning under edge computing setting, which involves AI techniques such as data augmentation and class estimation and balancing during training process with minimized computational overhead. This is accomplished through the implementation of data augmentation techniques to refine data distribution. Additionally, we leveraged class estimation and employed linear regression for client-side model training. This strategic approach yields a reduction in computational costs. To validate the effectiveness of the proposed approach, it is applied to two distinct datasets. One dataset pertains to image data (FashionMNIST), while the other comprises numerical and textual data concerning stocks for predictive analysis of stock values. This approach demonstrates commendable performance across both dataset types and approaching more than 92% of accuracy in the paradigm of federated learning.

Keywords

• Artificial intelligence
• Class estimation
• Data imbalance
• Distributed machine learning
• Federated learning
• Edge devices