Terahertz (THz) integrated sensing and communication (ISAC) is a promising enabler for 6G networks, offering ultra-high data rates and environment-aware capabilities. However, realizing its full potential requires accurate construction of directional THz radio maps and environment map from sparse and noisy signal measurements, which is a highly ill-posed problem. While recent generative models, particularly conditional diffusion models, show promise in radio map construction, they fail to capture the physical characteristics of THz signal propagation, limiting generalization. To address this, we propose a THz propagation-informed diffusion model that jointly generates multi-directional radio maps and infers the environment map via an image-intersection strategy. Crucially, our model embeds two novel physics-guided loss functions: the intra-beam propagation-informed loss and inter-beam environmental consistency loss, which enforce geometric and semantic fidelity on THz propagation behaviors into the training process. Simulation results demonstrate superior performance over existing methods across varying sensor densities and environment complexities.

Federated learning (FL) has the potential to empower Internet of Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this article, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a nontrivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties.

Distributed channel estimation (DCE) algorithm design is pivotal for exploiting the potential of cell-free massive multiple-input multiple-output (CF-mMIMO) systems, while reducing fronthaul costs and computational complexities inherent in conventional centralized algorithms. Most existing DCE algorithms utilize the linear minimum mean square error (LMMSE) estimator for its efficiency, but this method depends on precise channel covariance acquisition. To address these limitations, federated learning (FL)-based DCE algorithms, which use the widely adopted federated averaging protocol, have been developed for CF-mMIMO systems. However, in practical scenarios, small-scale fading at different access points often follows non-independent and identically distributed (non-IID) patterns. This non-IID nature causes existing FL-based DCE algorithms to suffer from model update variance during training, leading to slow convergence rates and poor inference performance. To address these challenges, we propose an efficient DCE algorithm built on a novel FL algorithm, specifically developed for this purpose, named Federated Variance Reduction (FedVR). Unlike federated averaging-based algorithms,FedVRcan reduce the variance of model updates between access points during training, resulting in faster convergence rates and improved channel estimation accuracy. Additionally, we theoretically prove the convergence of theFedVRalgorithm, and conduct extensive simulations to validate its efficacy.

The burgeoning complexity of communication necessitates a high demand for security. Access control encryption is a promising primitive to meet the security demand but the bulk of its constructions rely on formulating the access control policy with identities. Attribute-based access control policy in attribute-based encryption (ABE) is known to be more expressive without relying on enumerating identities. We propose a generic framework to build attribute-based access control encryption from ciphertext-policy ABE. Our instantiations prioritize different emphases on expressiveness and efficiency. The first instantiation supports multi-valued AND-gate access control structures, while the second supports the linear-secret-sharing access structure. Both are prototyped with efficiency validated empirically.

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance.

Obstacle sensing is essential for terahertz (THz) communication since the subsequent beam management can avoid THz signals blocked by the obstacles. In parallel, radio environment, which can be manifested by channel knowledge such as the distribution of received signal strength (RSS), reveals signal propagation situation and the corresponding obstacle information. However, the awareness of the radio environment for obstacle sensing is challenging in practice, as the sparsely deployed THz sensors can acquire only little a priori knowledge with their RSS measurements. Therefore, we formulate in this paper a radio environment awareness problem, which for the first time considers a probability distribution of obstacle attributes. To solve such a problem, we propose a THz-based generative radio environment awareness framework, in which obstacle information is obtained directly from the aware radio environment. We also propose a novel generative model based on conditional generative adversarial network (CGAN), where U-net and the objective function of the problem are introduced to enable accurate awareness of RSS distribution. Simulation results show that the proposed framework can improve the awareness of the radio environment, and thus achieve superior sensing performance in terms of average precision regarding obstacles’ shape and location.

Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients’ data. This paper delves into a class of federated problems characterized by non-convex and non-smooth loss functions, that are prevalent in FL applications but challenging to handle due to their intricate non-convexity and non-smoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this paper, we propose a novel federated primal-dual algorithm with bidirectional model sparsification tailored for non-convex and non-smooth FL problems, and differential privacy is applied for privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented as the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties.

In this article, we investigate the federated clustering (FedC) problem, which aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server (PS), meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a nonconvex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then, a novel FedC algorithm using differential privacy (DP) technique, referred to as DP- FedC, is proposed in which partial clients participation (PCP) and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP- FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of nonidentically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP- FedC. Then, some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP- FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.

Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Federated learning (FL) has emerged as a suitable and appealing distributed learning paradigm to deploy these applications at the network edge. Despite the many successful efforts made to apply FL to wireless edge networks, the adopted algorithms mostly follow the same spirit as FedAvg, thereby heavily suffering from the practical challenges of label deficiency and device heterogeneity. These challenges not only decelerate the model training in FL but also downgrade the application performance. In this paper, we focus on the algorithm design and address these challenges by investigating the personalized semi-supervised FL problem and proposing an effective algorithm, named FedCPSL. In particular, the techniques of pseudo-labeling, and interpolation-based model personalization are judiciously combined to provide a new problem formulation for personalized semi-supervised FL. The proposed FedCPSL algorithm employs novel strategies, including adaptive client variance reduction, local momentum, and normalized global aggregation, to combat the challenge of device heterogeneity and boost algorithm convergence. The convergence property of FedCPSL is also thoroughly analyzed and shows that FedCPSL is resilient to both statistical and system heterogeneity, obtaining a sublinear convergence rate. Experimental results on image classification tasks are presented to demonstrate that the proposed approach outperforms its counterparts in terms of both convergence speed and application performance.

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.

The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, traditional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, conventional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the system, which has been little studied in the literature. Furthermore, the proposed SLP scheme makes the “interference signals” to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. To resolve the optimization problem associated with the SLP scheme, we develop a fractional programming and successive upper-bound maximization based algorithm. Our simulation results demonstrate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.

Recently, federated learning (FL) has been increasingly appealing in distributed signal processing and machine learning. Nevertheless, the practical challenges of label deficiency and client heterogeneity form a bottleneck to its wide adoption. Although numerous efforts have been devoted to semi- supervised FL, most of the adopted algorithms follow the same spirit as FedAvg, thus heavily suffering from the adverse effects caused by client heterogeneity. In this paper, we boost the semi-supervised FL by addressing the issue using model personalization and client-variance-reduction. In particular, we propose a novel and unified problem formulation based on pseudo-labeling and model interpolation. We then propose an effective algorithm, named FedCPSL, which judiciously adopts the schemes of a novel momentum-based client- variance-reduction and normalized averaging. Convergence property of FedCPSL is analyzed and shows that FedCPSL is resilient to client heterogeneity and obtains a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate the efficacy of FedCPSL over the benchmark algorithms.

Federated learning (FL) is encountered with the challenge of training a model in massive and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm where parallel (stochastic) gradient descent (GD) is run on a small sampled subset of clients multiple times before uploading the local models to a server for averaging, which has been proven effective in reducing the communication cost for achieving a good model. However, MA has not been considered for the important matrix factorization (MF) model, which has vast signal processing and machine learning applications. In this paper, we investigate the federated MF problem and propose a new MA based algorithm, named FedMAvg, by judiciously combining the alternating minimization technique and MA. Through analysis, we show that gradually decreasing the number of local GD and only allowing partial clients to communicate with the server can greatly reduce the communication cost, especially in heterogeneous networks with non-i.i.d. data. Experimental results by applying FedMAvg to data clustering and item recommendation tasks demonstrate its efficacy in terms of both task performance and communication efficiency.

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found to provide improved clustering performance over the K-means. The ONMF model is a challenging optimization problem due to the orthogonality constraint, and most of the existing methods directly deal with the constraint in its original form via various optimization techniques. In this paper, we propose an equivalent problem reformulation that transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a penalty approach to handle these non-convex constraints. The penalized formulation is smooth and has convex constraints, which is amenable to efficient computation. We analytically show that the penalized formulation will provide a feasible stationary point of the reformulated ONMF problem when the penalty is large. Numerical results show that the proposed method greatly outperforms the existing methods.

Identification of cell subclasses using single-cell RNA-Sequencing (scRNA-Seq) data is of paramount importance since it uncovers the hidden biological processes within the cell population. While the nonnegative matrix factorization (NMF) model has been reported to be effective in various unsupervised clustering tasks, it may still produce inappropriate results for some scRNA-Seq datasets with heterogeneous structures. In this paper, we propose the use of an orthogonally constrained NMF (ONMF) model for the subclass identification problem of scRNA-Seq datasets. The ONMF model in general can provide improved clustering performance, but is challenging to solve. We present a computationally efficient algorithm based on optimization techniques of variable splitting and alternating direction method of multipliers (ADMM). Through two scRNA-Seq datasets, we show that the proposed method can yield promising performance in identifying cell subclasses and detecting key genes over the existing methods. Moreover, the key genes identified by the proposed method are shown biologically significant via the gene set enrichment analysis.