Radio Frequency Fingerprinting (RFF) exploits inherent hardware-level imperfections of wireless transmitters as unclonable identifiers for device identification. These unique signatures, concealed in transmitted signals, inevitably experience complex distortions during wireless propagation (i.e., coupled with ambient noise and channel fading), making it extremely challenging for reliable extraction. Despite substantial research efforts dedicated to advancing effective fingerprint extraction techniques, current approaches still struggle in handling fingerprint robustness under distance variations, leading to severe SNR fluctuations and complex multipath effects. To address this gap, we propose the first unsupervised framework for distance-invariant radio frequency fingerprinting, eliminating dependence on labeled target domain data. Specifically, we first preprocess raw RF samples by confining them within a specified variation range and filtering noisy high-frequency components while avoiding aliasing. For source domain data, we then propose a set of physics-inspired data augmentation techniques designed to emulate realistic wireless signal propagation effects. Building on this, we introduce a dual alignment contrastive learning method to explicitly decouple identity-discriminative features, ensuring the model focuses on device-specific traits. Furthermore, we incorporate a pseudo-labeling-based domain adaptation module to refine the model for the unlabeled target domain, enhancing its generalization to unseen distances. Extensive experiments on public datasets show that our method achieves the identification accuracy outperforming state-of-the-art approaches by 40%, while maintaining computational efficiency suitable for edge deployment.

Federated learning (FL) is a prevalent distributed machine learning framework that allows multiple clients to train one model by uploading gradients without sharing data, enabling cooperative learning while preserving the training data privacy. Nevertheless, recent research has revealed that shared gradients can still expose clients' private training data. These attacks, however, often become ineffective in two practical scenarios: (1) gradients are computed on high-resolution data; (2) labels are duplicated within the attacked batch. In this work, we introduce an advancedGradientLeakageAttack againstDuplicate labels (GLAD), which can effectively recover high-resolution training data from gradients while considering duplicate labels, making it applicable in more realistic FL scenarios. The key technique ofGLADis to formalize the relationships between model outputs, gradients, model parameters, and training data labels. Based on these relationships,GLADfurther reconstructs the model outputs and inverts the reconstructed model outputs back to the corresponding model inputs. Our method can achieve state-of-the-art recovery accuracy while ensuring efficiency. Extensive experimental results demonstrate thatGLADcan reconstruct images of 224× 224pixels with a batch size of 256 with duplicate labels. Our source code is available athttps://github.com/SuperX612/GLAD.

Call detail records (CDRs) provide valuable insights into user behavior, which are instrumental for telecom companies in optimizing network coverage and service quality. However, while cloud computing facilitates clustering analysis on a vast scale of CDR data, it introduces privacy risks. The challenge lies in striking a balance between efficiency, security, and cost-effectiveness in privacy-preserving algorithms. To tackle this issue, we propose a privacy-preserving and cost-effective incremental density peak clustering scheme. Our approach leverages homomorphic encryption and order-preserving encryption to enable direct computations and clustering on encrypted data. Moreover, it employs reaching definition analysis to optimize the execution flow of static tasks, pinpointing the optimal junctures for transitioning between the two types of encryption to reduce communication overhead. Furthermore, our scheme utilizes a game theory-based verification strategy to ascertain the accuracy of the results. This methodology can be effectively deployed on the Ethereum blockchain via smart contracts. A comprehensive security analysis confirms that our scheme upholds both privacy and data integrity. Experimental evaluations substantiate the clustering accuracy, communication load, and computational efficiency of our scheme, thereby validating its viability in real-world applications.

This article addresses the challenge of communication efficiency in federated learning by the proposed algorithm called global sparsification with adaptive aggregated stochastic gradients (GSASGs). GSASG leverages the advantages of local sparse communication, global sparsification communication, and adaptive aggregated gradients. More specifically, we devise an efficient global top-$k^{\prime }$sparsification operator. By applying this operator to the aggregated gradients obtained from the top-k sparsification, the global model parameter is rarefied to reduce the download transmitted bits from$O(dMT)$to$O(k^{\prime }MT)$, where d is the dimension of the gradient, M is the number of workers, T is the total number of epochs, and$k^{\prime } \leq k\lt d$. Meanwhile, the adaptive aggregated gradient method is adopted to skip meaningless communication and reduce communication rounds. The deep neural network training experiment demonstrates that, compared to the previous algorithms GSASG significantly reduces communication cost without sacrificing the model performance. For instance, when considering the MNIST data set with$k=1\% d$and$k^{\prime }=0.5\% d$, in terms of communication rounds, GSASG outperforms sparse communication by 91%, adaptive aggregated gradients by 90%, and the combination of sparse communication with adaptive aggregated gradients by 56%. In terms of communication bits, GSASG yields 1% of the communication bits needed with previous algorithms.

Ensuring secure communications for the Internet of Things (IoT) systems remains a challenge. Due to exacting resource limitations of computing, memory, and communication in IoT environments, communication schemes based on asymmetric cryptographic systems can be challenging to deploy. An alternative is to deploy symmetric encryption schemes based on pre-shared keys. However, there are also challenges in designing such schemes and examples include how to achieve an optimal trade-off between security and performance levels while meeting resource consumption requirements. Hence, this paper presents a lightweight key synchronization update algorithm, which is then used as a building block in our proposed lightweight secure communication protocol. The security of the protocol is analyzed to show that it can resist common attacks, such as replay attacks, and man-in-the-middle attacks. We then use Tamarin, a widely accepted security protocol verification tool, for formal verification. In addition, we evaluate the randomness and computational performance of the lightweight key synchronization update algorithm and demonstrate that it outperforms other schemes. We also evaluate the performance of the protocol, in terms of computational and communication costs, to demonstrate utility.