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 $(\sim \!\! 2.14\times $ and $\sim \!\! 1.1\times $ 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.

Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users’ locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users’ location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.

Federated Learning (FL) enables collaborative model training across untrusted devices while preserving data privacy. However, malicious participants can still launch attacks, especially poisoning attacks, during model aggregation. To defend against poisoning attacks, we propose a Coulomb Force-based Federated Learning (CFFL), a physics-inspired defense framework that integrates neighborhood Coulomb force into FL security mechanisms. CFFL effectively addresses detection failures in poisoning attacks arising from the “distance concentration” among high-dimensional data. Specifically, firstly, we establishes a local update model for clients, where the similarity between model updates is quantified through local Coulomb resultant force (LCRF), effectively distinguishing malicious and benign updates; Secondly, we develop a k-nearest neighbor (KNN)-based Coulomb resultant force anomaly detection (NCFAD) model that identifies malicious updates by isolating top-k outliers with largest neighborhood factor; Finally, experiment results validate that CFFL outperforms state-of-the-art (SOTA) baselines in defense performance and achieves model accuracy equivalent to the baseline FedAvg on NCI-1, PROTEINS_full, and MNIST datasets (90.82%, 92.23%, and 96.49%, respectively). This indicates that CFFL effectively mitigates the impact of poisoning attacks without compromising the benign performance of the aggregation model.