Socially-Aware-Clustering-Enabled Federated Learning for Edge Networks

Abstract

Edge Intelligence based on federated learning (FL) can be considered to be a promising paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a rapid upsurge in the number of IoT devices is expected in the foreseeable future. Although FL enables privacy-preserving, on-device machine learning, it still exhibits a privacy leakage issue. A malicious aggregation server can infer the sensitive information of other end-devices using their local learning model updates. Furthermore, centralized FL aggregation server might stop working due to security attack or a physical damage. To address the aforementioned issues, we propose a novel concept of socially-aware-clustering-enabled dispersed FL. First, we present a novel framework for socially-aware-clustering-enabled dispersed FL. Second, we formulate a problem for minimizing the loss function of the proposed FL scheme. Third, we decompose the formulated problem into three sub-problems, such as local devices relative accuracy minimization (i.e., end-devices local accuracy maximization) sub-problem, clustering sub-problem, and resource allocation sub-problem, due to the NP-hard nature of the formulated problem. The clustering and resource allocation sub-problems are solved using low complexity schemes based on a matching theory. The end devices' relative accuracy minimization problem is solved by using a convex optimizer. Finally, numerical results are provided for validation of the proposed FL scheme. Furthermore, we show the convergence of the proposed FL scheme for image classification tasks using the MNIST dataset.