arXiv subjects

Wei Ni

Publications and source records attributed to Wei Ni.

At least 91 records · Page 5Linked to original sources

arXiv2025

Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation

Traditional CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) schemes are increasingly vulnerable to automated attacks powered by deep neural networks (DNNs). Existing adversarial attack methods often rely on the original image characteristics, resulting in distortions that hinder human interpretation and limit their applicability in scenarios where no initial input images are available. To address these challenges, we propose the Unsourced Adversarial CAPTCHA (DAC), a novel framework that generates high-fidelity adversarial examples guided by attacker-specified semantics information. Leveraging a Large Language Model (LLM), DAC enhances CAPTCHA diversity and enriches the semantic information. To address various application scenarios, we examine the white-box targeted attack scenario and the black box untargeted attack scenario. For target attacks, we introduce two latent noise variables that are alternately guided in the diffusion step to achieve robust inversion. The synergy between gradient guidance and latent variable optimization achieved in this way ensures that the generated adversarial examples not only accurately align with the target conditions but also achieve optimal performance in terms of distributional consistency and attack effectiveness. In untargeted attacks, especially for black-box scenarios, we introduce bi-path unsourced adversarial CAPTCHA (BP-DAC), a two-step optimization strategy employing multimodal gradients and bi-path optimization for efficient misclassification. Experiments show that the defensive adversarial CAPTCHA generated by BP-DAC is able to defend against most of the unknown models, and the generated CAPTCHA is indistinguishable to both humans and DNNs.

cs.CV↗

arXiv2025

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?

Inherent communication noises have the potential to preserve privacy for wireless federated learning (WFL) but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially catastrophic consequences of bit errors in floating-point numbers, e.g., on the sign or exponent bits. This paper presents a novel channel-native bit-flipping differential privacy (DP) mechanism tailored for WFL, where transmit bits are randomly flipped and communication noises are leveraged, to collectively preserve the privacy of WFL in digital communication systems. The key idea is to interpret the bit perturbation at the transmitter and bit errors caused by communication noises as a bit-flipping DP process. This is achieved by designing a new floating-point-to-fixed-point conversion method that only transmits the bits in the fraction part of model parameters, hence eliminating the need for transmitting the sign and exponent bits and preventing the catastrophic consequence of bit errors. We analyze a new metric to measure the bit-level distance of the model parameters and prove that the proposed mechanism satisfies (λ,ε)-Rényi DP and does not violate the WFL convergence. Experiments validate privacy and convergence analysis of the proposed mechanism and demonstrate its superiority to the state-of-the-art Gaussian mechanisms that are channel-agnostic and add Gaussian noise for privacy protection.

cs.LG↗

arXiv2025

A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

cs.DC↗

arXiv2025

Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients' concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy.

cs.LG↗

arXiv2025

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.

cs.LG↗

arXiv2025

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differential privacy (DP) mechanism. We analyze the convergence upper bounds of individual PL models by considering the impact of the mechanism (i.e., quantization errors and Gaussian DP noises) and imperfect communication channels on the FL of WPFL. By minimizing the maximum of the bounds, we design an optimal transmission scheduling strategy that yields min-max fairness for WPFL with OFDMA interfaces. This is achieved by revealing the nested structure of this problem to decouple it into subproblems solved sequentially for the client selection, channel allocation, and power control, and for the learning rates and PL-FL weighting coefficients. Experiments validate our analysis and demonstrate that our approach substantially outperforms alternative scheduling strategies by 87.08%, 16.21%, and 38.37% in accuracy, the maximum test loss of participating clients, and fairness (Jain's index), respectively.

cs.DC↗

arXiv2025

Towards Quantum-Native Communication Systems: State-of-the-Art, Trends, and Challenges

The potential synergy between quantum communications and future wireless communication systems is explored. By proposing a quantum-native or quantum-by-design philosophy, the survey examines technologies such as quantumdomain (QD) multi-input multi-output, QD non-orthogonal multiple access, quantum secure direct communication, QD resource allocation, QD routing, and QD artificial intelligence. The recent research advances in these areas are summarized. Given the behavior of photonic and particle-like Terahertz (THz) systems, a comprehensive system-oriented perspective is adopted to assess the feasibility of using quantum communications in future systems. This survey also reviews quantum optimization algorithms and quantum neural networks to explore the potential integration of quantum communication and quantum computing in future systems. Additionally, the current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications. The associated research gaps and future directions are identified, including extending the entanglement coherence time, developing THz quantum communications devices, addressing challenges in channel estimation and tracking, and establishing the theoretical bounds and performance trade-offs of quantum communication, computing, and sensing. This survey offers a unique perspective on the potential for quantum communications to revolutionize future systems and pave the way for even more advanced technologies.

quant-ph↗

arXiv2025

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments

Federated Learning (FL) is a distributed machine learning paradigm based on protecting data privacy of devices, which however, can still be broken by gradient leakage attack via parameter inversion techniques. Differential privacy (DP) technology reduces the risk of private data leakage by adding artificial noise to the gradients, but detrimental to the FL utility at the same time, especially in the scenario where the data is Non-Independent Identically Distributed (Non-IID). Based on the impact of heterogeneous data on aggregation performance, this paper proposes a Lightweight Adaptive Privacy Allocation (LAPA) strategy, which assigns personalized privacy budgets to devices in each aggregation round without transmitting any additional information beyond gradients, ensuring both privacy protection and aggregation efficiency. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to optimize the transmission power, in order to determine the optimal timing at which the adaptively attenuated artificial noise aligns with the communication noise, enabling an effective balance between DP and system utility. Finally, a reliable aggregation strategy is designed by integrating communication quality and data distribution characteristics, which improves aggregation performance while preserving privacy. Experimental results demonstrate that the personalized noise allocation and dynamic optimization strategy based on LAPA proposed in this paper enhances convergence performance while satisfying the privacy requirements of FL.

cs.LG↗

arXiv2025

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

cs.CR↗

arXiv2025

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments

The aggregation efficiency and accuracy of wireless Federated Learning (FL) are significantly affected by resource constraints, especially in heterogeneous environments where devices exhibit distinct data distributions and communication capabilities. This paper proposes a clustering strategy that leverages prior knowledge similarity to group devices with similar data and communication characteristics, mitigating performance degradation from heterogeneity. On this basis, a novel Cluster- Aware Multi-round Update (CAMU) strategy is proposed, which treats clusters as the basic units and adjusts the local update frequency based on the clustered contribution threshold, effectively reducing update bias and enhancing aggregation accuracy. The theoretical convergence of the CAMU strategy is rigorously validated. Meanwhile, based on the convergence upper bound, the local update frequency and transmission power of each cluster are jointly optimized to achieve an optimal balance between computation and communication resources under constrained conditions, significantly improving the convergence efficiency of FL. Experimental results demonstrate that the proposed method effectively improves the model performance of FL in heterogeneous environments and achieves a better balance between communication cost and computational load under limited resources.

cs.LG↗

arXiv2025

DeFeed: Secure Decentralized Cross-Contract Data Feed in Web 3.0 for Connected Autonomous Vehicles

Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contracts enable trustless interactions without the need for intermediaries, as they operate based on predefined rules encoded on the blockchain. However, smart contacts face significant challenges in cross-contract communication and information sharing, making it difficult to establish seamless connectivity and collaboration among CAVs with Web 3.0. In this paper, we propose DeFeed, a novel secure protocol that incorporates various gas-saving functions for CAVs, originated from in-depth research into the interaction among smart contracts for decentralized cross-contract data feed in Web 3.0. DeFeed allows smart contracts to obtain information from other contracts efficiently in a single click, without complicated operations. We judiciously design and complete various functions with DeFeed, including a pool function and a cache function for gas optimization, a subscribe function for facilitating data access, and an update function for the future iteration of our protocol. Tailored for CAVs with Web 3.0 use cases, DeFeed enables efficient data feed between smart contracts underpinning decentralized applications and vehicle coordination. Implemented and tested on the Ethereum official test network, DeFeed demonstrates significant improvements in contract interaction efficiency, reducing computational complexity and gas costs. Our solution represents a critical step towards seamless, decentralized communication in Web 3.0 ecosystems.

cs.CR↗

arXiv2025

Reconfigurable Intelligent Surface-Assisted Localization in OFDM Systems with Carrier Frequency Offset and Phase Noise

Reconfigurable intelligent surface (RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact of carrier frequency offset (CFO) and phase noise (PN) resulting from hardware impairments on localization. This paper presents a novel, alternating optimization (AO)-based algorithm to jointly estimate the CFO, PN, and user equipment (UE) position in orthogonal frequency division multiplexing (OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than $\rm 10^{-2} \: m$.

eess.SP↗

arXiv2025

HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fine-tuning these models with private data for diverse downstream tasks has become mainstream. Though federated learning (FL) offers a promising solution for fine-tuning LLMs without sharing raw data, substantial computing costs hinder its democratization. Moreover, in real-world scenarios, private client devices often possess heterogeneous computing resources, further complicating LLM fine-tuning. To combat these challenges, we propose HSplitLoRA, a heterogeneous parameter-efficient fine-tuning (PEFT) framework built on split learning (SL) and low-rank adaptation (LoRA) fine-tuning, for efficiently fine-tuning LLMs on heterogeneous client devices. HSplitLoRA first identifies important weights based on their contributions to LLM training. It then dynamically configures the decomposition ranks of LoRA adapters for selected weights and determines the model split point according to varying computing budgets of client devices. Finally, a noise-free adapter aggregation mechanism is devised to support heterogeneous adapter aggregation without introducing noise. Extensive experiments demonstrate that HSplitLoRA outperforms state-of-the-art benchmarks in training accuracy and convergence speed.

cs.LG↗

arXiv2025

Lightweight and Self-Evolving Channel Twinning: An Ensemble DMD-Assisted Approach

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.

eess.SP↗

arXiv2025

Building Sustainable and Trustworthy Indigenous Knowledge Preservation Ecosystem

This paper focuses on the essential global issue of protecting and transmitting indigenous knowledge. It reveals the challenges in this area and proposes a sustainable supply chain framework for indigenous knowledge. The paper reviews existing technological solutions and identifies technical challenges and gaps. It then introduces cutting-edge technologies to protect and disseminate indigenous knowledge more effectively. The paper also discusses how the proposed framework can address real-world challenges in protecting and transmitting indigenous knowledge, and explores future research applications of the proposed solutions. Finally, it addresses open issues and provides a detailed analysis, offering promising research directions for the protection and transmission of indigenous knowledge worldwide.

cs.CY↗

arXiv2025

Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders

EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensive and may compromise privacy. Instead, federated learning allows each device to upload a shared machine-learning model update with locally processed data. However, this technique, which depends on aggregating model updates from various IoT devices, is vulnerable to attacks from malicious entities that may inject harmful data into the learning process. This paper introduces a new attack method targeting federated learning in EdgeIoT, known as data-independent model manipulation attack. This attack does not rely on training data from the IoT devices but instead uses an adversarial variational graph auto-encoder (AV-GAE) to create malicious model updates by analyzing benign model updates intercepted during communication. AV-GAE identifies and exploits structural relationships between benign models and their training data features. By manipulating these structural correlations, the attack maximizes the training loss of the federated learning system, compromising its overall effectiveness.

cs.LG↗

arXiv2025

ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning

Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on manual criteria and labels, making them labor-intensive. Large language models (LLM) can minimize human effort but struggle with errors requiring a comprehensive understanding of data context. In this paper, we propose ZeroED, a novel hybrid zero-shot error detection framework, which combines LLM reasoning ability with the manual label-based ED pipeline. ZeroED operates in four steps, i.e., feature representation, error labeling, training data construction, and detector training. Initially, to enhance error distinction, ZeroED generates rich data representations using error reason-aware binary features, pre-trained embeddings, and statistical features. Then, ZeroED employs LLM to label errors holistically through in-context learning, guided by a two-step reasoning process for detailed error detection guidelines. To reduce token costs, LLMs are applied only to representative data selected via clustering-based sampling. High-quality training data is constructed through in-cluster label propagation and LLM augmentation with verification. Finally, a classifier is trained to detect all errors. Extensive experiments on seven public datasets demonstrate that, ZeroED substantially outperforms state-of-the-art methods by a maximum 30% improvement in F1 score and up to 90% token cost reduction.

cs.LG↗

arXiv2025

Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends

In this survey, we investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks, focusing on joint training with agglomerative deduction and feature-oriented security mechanisms. We explore adaptive hierarchical learning strategies to tackle non-IID data challenges, improving scalability and reducing communication overhead. Fault tolerance techniques and agglomerative deduction mechanisms are studied to detect unreliable devices, refine model updates, and enhance convergence stability. Unlike existing FL security research, we comprehensively analyze feature-oriented threats, such as poisoning, inference, and reconstruction attacks that exploit model features. Moreover, we examine resilient aggregation techniques, anomaly detection, and cryptographic defenses, including differential privacy and secure multi-party computation, to strengthen FL security. In addition, we discuss the integration of 6G, large language models (LLMs), and interoperable learning frameworks to enhance privacy-preserving and decentralized cross-domain training. These advancements offer ultra-low latency, artificial intelligence (AI)-driven network management, and improved resilience against adversarial attacks, fostering the deployment of secure ResFL in CyberEdge networks.

cs.CR↗