arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 271 records · Page 15Linked to original sources

arXiv2024

Secure Communications in Near-Filed ISCAP Systems with Extremely Large-Scale Antenna Arrays

This paper investigates secure communications in a near-field multi-functional integrated sensing, communication, and powering (ISCAP) system with an extremely large-scale antenna arrays (ELAA) equipped at the base station (BS). In this system, the BS sends confidential messages to a single communication user (CU), and at the same time wirelessly senses a point target and charges multiple energy receivers (ERs). It is assumed that the ERs and the sensing target are potential eavesdroppers that may attempt to intercept the confidential messages intended for the CU. We consider the joint transmit beamforming design to support secure communications while ensuring the sensing and powering requirements. In particular, the BS transmits dedicated sensing/energy beams in addition to the information beam, which also play the role of artificial noise (AN) for effectively jamming potential eavesdroppers. Building upon this, we maximize the secrecy rate at the CU, subject to the maximum \ac{crb} constraints for target sensing and the minimum harvested energy constraints for the ERs. Although the formulated joint beamforming problem is non-convex and challenging to solve, we acquire the optimal solution via the semi-definite relaxation (SDR) and fractional programming techniques together with a one-dimensional (1D) search. Subsequently, we present two alternative designs based on zero-forcing (ZF) beamforming and maximum ratio transmission (MRT), respectively. Finally, our numerical results show that our proposed approaches exploit both the distance-domain resolution of near-field ELAA and the joint beamforming design for enhancing secure communication performance while ensuring the sensing and powering requirements in ISCAP, especially when the CU and the target and ER eavesdroppers are located at the same angle (but different distances) with respect to the BS.

eess.SP↗

arXiv2024

Accelerating Hybrid Federated Learning Convergence under Partial Participation

Over the past few years, Federated Learning (FL) has become a popular distributed machine learning paradigm. FL involves a group of clients with decentralized data who collaborate to learn a common model under the coordination of a centralized server, with the goal of protecting clients' privacy by ensuring that local datasets never leave the clients and that the server only performs model aggregation. However, in realistic scenarios, the server may be able to collect a small amount of data that approximately mimics the population distribution and has stronger computational ability to perform the learning process. To address this, we focus on the hybrid FL framework in this paper. While previous hybrid FL work has shown that the alternative training of clients and server can increase convergence speed, it has focused on the scenario where clients fully participate and ignores the negative effect of partial participation. In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed. We then propose a new algorithm called FedCLG, which investigates the two-fold role of the server in hybrid FL. Firstly, the server needs to process the training steps using its small amount of local datasets. Secondly, the server's calculated gradient needs to guide the participated clients' training and the server's aggregation. We validate our theoretical findings through numerical experiments, which show that our proposed method FedCLG outperforms state-of-the-art methods.

cs.DC↗

arXiv2024

Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges

This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, and evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.

cs.CL↗

arXiv2024

Networked ISAC for Low-Altitude Economy: Transmit Beamforming and UAV Trajectory Design

This paper studies the exploitation of networked integrated sensing and communications (ISAC) to support low-altitude economy (LAE), in which a set of networked ground base stations (GBSs) transmit wireless signals to cooperatively communicate with multiple authorized unmanned aerial vehicles (UAVs) and concurrently use the echo signals to detect the invasion of unauthorized objects in interested airspace. Under this setup, we jointly design the cooperative transmit beamforming at multiple GBSs together with the trajectory control of authorized UAVs and their GBS associations, for enhancing the authorized UAVs' communication performance while ensuring the sensing requirements for airspace monitoring. In particular, our objective is to maximize the average sum rate of authorized UAVs over a particular flight period, subject to the minimum illumination power constraints for sensing over the interested airspace, the maximum transmit power constraints at individual GBSs, and the flight constraints at UAVs. This problem is non-convex and challenging to solve, due to the involvement of integer variables and the coupling of optimization variables. To solve this non-convex problem, we propose an efficient algorithm by using the techniques of alternating optimization (AO), successive convex approximation (SCA), and semi-definite relaxation (SDR). Numerical results show that the obtained transmit beamforming and UAV trajectory designs in the proposed algorithm efficiently balance the tradeoff between the sensing and communication performances, thus significantly outperforming various benchmarks.

eess.SP↗

arXiv2024

Joint Signal Detection and Automatic Modulation Classification via Deep Learning

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies. We first generate a coexisting RADIOML dataset (CRML23) to facilitate the joint design. Different from the publicly available AMC dataset ignoring the signal detection step and containing only one signal, our synthetic dataset covers the more realistic multiple-signal coexisting scenario. Then, we present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment, which consists of two modules for signal detection and AMC, respectively. In particular, these two modules are interconnected using a designated data structure called "proposal". Finally, we conduct extensive simulations over the newly developed dataset, which demonstrate the effectiveness of our designs. Our code and dataset are now available as open-source (https://github.com/Singingkettle/ChangShuoRadioData).

eess.SP↗

arXiv2024

Joint Transmit and Reflective Beamforming for Multi-Active-IRS-Assisted Cooperative Sensing

This paper studies multi-active intelligent-reflecting-surface (IRS) cooperative sensing, in which multiple active IRSs are deployed in a distributed manner to help the base station (BS) provide multi-view sensing. We focus on the scenario where the sensing target is located in the non-line-of-sight (NLoS) area of the BS. Based on the received echo signal, the BS aims to estimate the target's direction-of-arrival (DoA) with respect to each IRS. In addition, we leverage active IRSs to overcome the severe path loss induced by multi-hop reflections. Under this setup, we minimize the maximum Cramér-Rao bound (CRB) among all IRSs by jointly optimizing the transmit beamforming at the BS and the reflective beamforming at the multiple IRSs, subject to the constraints on the maximum transmit power at the BS, as well as the maximum transmit power and the maximum power amplification gain at individual IRSs. To tackle the resulting highly non-convex max-CRB minimization problem, we propose an efficient algorithm based on alternating optimization, successive convex approximation, and semi-definite relaxation, to obtain a high-quality solution. Finally, numerical results are provided to verify the effectiveness of our proposed design and the benefits of active IRS-assisted sensing compared to the counterpart with passive IRSs.

cs.IT↗

arXiv2024

LMEraser: Large Model Unlearning through Adaptive Prompt Tuning

To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser. Existing unlearning research suffers from entangled training data and complex model architectures, incurring extremely high computational costs for large models. LMEraser takes a divide-and-conquer strategy with a prompt tuning architecture to isolate data influence. The training dataset is partitioned into public and private datasets. Public data are used to train the backbone of the model. Private data are adaptively clustered based on their diversity, and each cluster is used to optimize a prompt separately. This adaptive prompt tuning mechanism reduces unlearning costs and maintains model performance. Experiments demonstrate that LMEraser achieves a $100$-fold reduction in unlearning costs without compromising accuracy compared to prior work. Our code is available at: \url{https://github.com/lmeraser/lmeraser}.

cs.LG↗

arXiv2024

Adaptive User-Centric Entanglement Routing in Quantum Data Networks

Distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quantum data network (QDN). Establishing long-distance quantum entanglement between two QCs for quantum teleportation within the QDN is a critical aspect, and it involves entanglement routing - finding a route between QCs and efficiently allocating qubits along that route. Existing approaches have mainly focused on optimizing entanglement performance for current entanglement connection (EC) requests. However, they often overlook the user's perspective, wherein the user making EC requests operates under a budget constraint over an extended period. Furthermore, both QDN resources (quantum channels and qubits) and the EC requests, reflecting the DQC workload, vary over time. In this paper, we present a novel user-centric entanglement routing problem that spans an extended period to maximize the entanglement success rate while adhering to the user's budget constraint. To address this challenge, we leverage the Lyapunov drift-plus-penalty framework to decompose the long-term optimization problem into per-slot problems, allowing us to find solutions using only the current system information. Subsequently, we develop efficient algorithms based on continuous-relaxation and Gibbs-sampling techniques to solve the per-slot entanglement routing problem. Theoretical performance guarantees are provided for both the per-slot and long-term problems. Extensive simulations demonstrate that our algorithm significantly outperforms baseline approaches in terms of entanglement success rate and budget adherence.

quant-ph↗

arXiv2024

AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent

Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning. The efficacy of imitation learning is heavily reliant on the quantity and quality of the demonstration datasets. In this study, we aim to scale up demonstrations in a data-efficient way to facilitate the learning of generalist robotic agents. We introduce AdaDemo (Adaptive Online Demonstration Expansion), a general framework designed to improve multi-task policy learning by actively and continually expanding the demonstration dataset. AdaDemo strategically collects new demonstrations to address the identified weakness in the existing policy, ensuring data efficiency is maximized. Through a comprehensive evaluation on a total of 22 tasks across two robotic manipulation benchmarks (RLBench and Adroit), we demonstrate AdaDemo's capability to progressively improve policy performance by guiding the generation of high-quality demonstration datasets in a data-efficient manner.

cs.RO↗

arXiv2024

Random ISAC Signals Deserve Dedicated Precoding

Radar systems typically employ well-designed deterministic signals for target sensing, while integrated sensing and communications (ISAC) systems have to adopt random signals to convey useful information. This paper analyzes the sensing and ISAC performance relying on random signaling in a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over random ISAC signals. Then, we investigate a data-dependent precoding (DDP) scheme to minimize the ELMMSE in sensing-only scenarios, which attains the optimized performance at the cost of high implementation overhead. To reduce the cost, we present an alternative data-independent precoding (DIP) scheme by stochastic gradient projection (SGP). Moreover, we shed light on the optimal structures of both sensing-only DDP and DIP precoders. As a further step, we extend the proposed DDP and DIP approaches to ISAC scenarios, which are solved via a tailored penalty-based alternating optimization algorithm. Our numerical results demonstrate that the proposed DDP and DIP methods achieve substantial performance gains over conventional ISAC signaling schemes that treat the signal sample covariance matrix as deterministic, which proves that random ISAC signals deserve dedicated precoding designs.

cs.IT↗

arXiv2024

Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm

In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fine-tuning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. In this system, model pre-training is first conducted at an edge server via centralized learning on local pre-stored general data, and then task-specific fine-tuning is performed at edge devices based on the pre-trained model via federated edge learning. For the two-stage learning model, we first analyze the convergence behavior (in terms of the average squared gradient norm bound), which characterizes the impacts of various system parameters such as the number of learning rounds and batch sizes in the two stages on the convergence rate. Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay. The decision variables include the number of learning rounds, batch sizes, clock frequencies, and transmit power control for both pre-training and fine-tuning stages. Finally, numerical results are provided to evaluate the effectiveness of our proposed design. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off among the training accuracy, delay, and energy consumption. The proposed design is also shown to effectively leverage the inherent trade-off between pre-training and fine-tuning, which arises from the differences in data distribution between pre-stored general data versus real-time task-specific data, thus efficiently optimizing overall system performance.

cs.IT↗

arXiv2024

Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios

Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners. Multiple views provide more information than single views and thus existing MVC methods can achieve satisfactory performance. However, their performance might seriously degenerate when the views are noisy in practical multi-view scenarios. In this paper, we formally investigate the drawback of noisy views and then propose a theoretically grounded deep MVC method (namely MVCAN) to address this issue. Specifically, we propose a novel MVC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore, a two-level multi-view iterative optimization is designed to generate robust learning targets for refining individual views' representation learning. Theoretical analysis reveals that MVCAN works by achieving the multi-view consistency, complementarity, and noise robustness. Finally, experiments on extensive public datasets demonstrate that MVCAN outperforms state-of-the-art methods and is robust against the existence of noisy views.

cs.LG↗

arXiv2024

Energy-Efficient Hybrid Beamforming with Dynamic On-off Control for Integrated Sensing, Communications, and Powering

This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cramér-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex. Please refer to the paper for a complete abstract.

cs.IT↗

arXiv2024

Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying

This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the server is always available, however, this is not the case in many real-world systems. To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL. By exploiting mobility, the study shows that clients can indirectly communicate with the server through another client serving as a relay, creating additional communication opportunities. This enables clients to upload local model updates sooner or receive fresher global models. We propose a new FL algorithm, called FedMobile, that incorporates opportunistic relaying and addresses key questions such as when and how to relay. We prove that FedMobile achieves a convergence rate $O(\frac{1}{\sqrt{NT}})$, where $N$ is the number of clients and $T$ is the number of communication slots, and show that the optimal design involves an interesting trade-off on the best timing of relaying. The paper also presents an extension that considers data manipulation before relaying to reduce the cost and enhance privacy. Experiment results on a synthetic dataset and two real-world datasets verify our theoretical findings.

cs.LG↗

arXiv2024

Machine Unlearning: Solutions and Challenges

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of the solutions in machine unlearning. We categorize existing solutions into exact unlearning approaches that remove data influence thoroughly and approximate unlearning approaches that efficiently minimize data influence. By comprehensively reviewing solutions, we identify and discuss their strengths and limitations. Furthermore, we propose future directions to advance machine unlearning and establish it as an essential capability for trustworthy and adaptive machine learning models. This paper provides researchers with a roadmap of open problems, encouraging impactful contributions to address real-world needs for selective data removal.

cs.LG↗

arXiv2024

FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate access to users' raw data, posing substantial privacy risks. While Federated Learning (FL) serves as a privacy-preserving alternative, it falls short in addressing the challenges posed by heterogeneous (yet possibly overlapped) modalities data across various hospitals. To bridge this gap, we propose a Federated Multi-Modal (FedMM) learning framework that federatedly trains multiple single-modal feature extractors to enhance subsequent classification performance instead of existing FL that aims to train a unified multimodal fusion model. Any participating hospital, even with small-scale datasets or limited devices, can leverage these federated trained extractors to perform local downstream tasks (e.g., classification) while ensuring data privacy. Through comprehensive evaluations of two publicly available datasets, we demonstrate that FedMM notably outperforms two baselines in accuracy and AUC metrics.

cs.CV↗

arXiv2024

SInViG: A Self-Evolving Interactive Visual Agent for Human-Robot Interaction

Linguistic ambiguity is ubiquitous in our daily lives. Previous works adopted interaction between robots and humans for language disambiguation. Nevertheless, when interactive robots are deployed in daily environments, there are significant challenges for natural human-robot interaction, stemming from complex and unpredictable visual inputs, open-ended interaction, and diverse user demands. In this paper, we present SInViG, which is a self-evolving interactive visual agent for human-robot interaction based on natural languages, aiming to resolve language ambiguity, if any, through multi-turn visual-language dialogues. It continuously and automatically learns from unlabeled images and large language models, without human intervention, to be more robust against visual and linguistic complexity. Benefiting from self-evolving, it sets new state-of-the-art on several interactive visual grounding benchmarks. Moreover, our human-robot interaction experiments show that the evolved models consistently acquire more and more preferences from human users. Besides, we also deployed our model on a Franka robot for interactive manipulation tasks. Results demonstrate that our model can follow diverse user instructions and interact naturally with humans in natural language, despite the complexity and disturbance of the environment.

cs.RO↗

arXiv2024

Integrating Sensing, Communication, and Power Transfer: From Theory to Practice

To support the development of internet-of-things applications, an enormous population of low-power devices are expected to be incorporated in wireless networks performing sensing and communication tasks. As a key technology for improving the data collection efficiency, integrated sensing and communication (ISAC) enables simultaneous data transmission and radar sensing by reusing the same radio signals. In addition to information carriers, wireless signals can also serve as energy delivers, which enables simultaneous wireless information and power transfer (SWIPT). To improve the energy and spectrum efficiency, the advantages of ISAC and SWIPT are expected to be exploited, leading to the emerging technology of integrating sensing, communication, and power transfer (ISCPT). In this article, a timely overview of ISCPT is provided with the description of the fundamentals, the characterization of the theoretical boundary, the discussion on the key technologies, and the demonstration of the implementation platform.

cs.IT↗