FRAD: Free-Rider Attacks Detection Mechanism for Federated Learning in AIoT

Abstract

The rapid development of the Artificial Intelligence of Things (AIoT) opens up a new perspective for emerging service-based applications and becomes a major driver of diverse federated learning (FL) applications. However, due to the heterogeneity of nodes and the existence of free-rider attacks, some device nodes may launch free-rider attacks to obtain the global model without any contribution, which not only dampens the enthusiasm of legitimate participants but also undermines the fairness of node contributions. In this article, we propose a free-rider attack detection (FRAD) mechanism for FL with deep autoencoding Gaussian mixture model (DAGMM) based on contribution and reputation. Specifically, we first model the contribution values based on the computing resource, communication cost, and data quality of each device node. Then, based on PageRank algorithms, we design an optimal reputation-based model to fairly and precisely choose benign nodes to participate in federated training under information asymmetry. Furthermore, we develop the FRAD mechanism via DAGMM that combines historical contribution with reputation scores. Simulation results validate that the proposed mechanism in this article outperforms the state-of-the-art baselines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\sim \!\! 2.14\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim \!\! 1.1\times $ </tex-math></inline-formula> on MNIST and CIFAR, respectively) in defending against free-rider attacks, and when the main clients are free-riders, i.e., 50% or even up to 80% of the free-riders, FRAD can still maintain a high defense performance against free-rider attacks.