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Decentralized Unlearning for Trustworthy AI-Generated Content (AIGC) Services

IEEE Network 2025年第3期39卷 285-292页

作者： Lin, Yijing Du, Hongyang Gao, Zhipeng Yao, Jing Jiang, Bingting Niyato, Dusit Li, Ruidong Zhang, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China The University of Hong Kong Department of Electrical and Electronic Engineering Pokfulam Hong Kong Central South University School of Computer Science and Engineering Changsha410083 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Kanazawa University Institute of Science and Engineering Kanazawa920-1192 Japan

The widespread adoption of AI-Generated Content (AIGC) has captured remarkable interests from academia and industry. However, this advancement brings forth challenges in data ownership, as emphasized by the General Data Protection Regulation’s right to be forgotten. The right to be forgotten allows participants to erase their data contributions from well-trained AIGC models. Training AIGC models demands an extensive amount of data and computations, often surpassing the capabilities of individual participants. As a solution, Federated Learning (FL) is employed to collaboratively train an AIGC model without sharing raw data directly. To protect the right to be forgotten in FL, "federated unlearning" has been introduced to eradicate the data contributions of unlearning participants from fully trained AIGC models, achieved by deleting historical model updates. However, given that historical updates are distributed among various participants, they may still hold contributions from the unlearning participants. This presents a challenge to the trustworthiness of federated unlearning. In this paper, we first provide an overview of both centralized and federated learning and unlearning methods. We survey relevant works and classify four main categories for federated unlearning. Subsequently, we introduce a comprehensive decentralized unlearning architecture designed for trustworthy AIGC services. To address the challenge of participants retaining historical contributions from unlearning participants, we integrate a coded computing-based interaction mechanism, ensuring both scalability and security. We further present a case study using NanoGPT to demonstrate the effectiveness of the proposed framework for decentralized unlearning. We conclude by identifying potential future research directions. © 1986-2012 IEEE.

关键词： Network security

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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The Impact of the 2024 Total Solar Eclipse on the Power System Frequency of US Interconnections: A Case Study

The Impact of the 2024 Total Solar Eclipse on the Power Syst...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Dulal, Saurav Wen, Yan Liu, Yilu The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331504847

With the deployment of wide-area monitoring systems, power systems can be continuously monitored and analyzed, particularly during unusual events. Frequency fluctuations in the United States (US) interconnected power grid are influenced by various factors, including weather conditions and variability in solar power generation. This paper investigates the impact of the solar eclipse on April 8, 2024, on the frequency stability of the US power grid. Phasor measurement data from multiple interconnections are collected and analyzed. Methods of approximate frequency estimation for each interconnection are employed. The study involves comparing the frequency distribution, probability density, and variance on the eclipse day with those of non-eclipse days. The results provide insight into the grid's response to reduced solar generation and underscore the importance of predictive measures to maintain stability during solar eclipses and similar disruptions. The study shows that the Electric Reliability Council of Texas (ERCOT) experienced a dramatic increase in frequency fluctuations - 198% higher compared to periods without eclipse - highlighting the grid's vulnerability to reduced solar irradiance. Conversely, Eastern Interconnection (EI) demonstrated remarkable stability during the eclipse with minimal frequency fluctuations. © 2025 IEEE.

关键词： Electric power transmission networks

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Stolt Interpolation-Free Range Migration Algorithm for Dynamic-Aperture-Based Computational MIMO Microwave Imaging

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IEEE Access 2025年 13卷 83082-83094页

作者： Skouroliakou, Vasiliki García-Fernandez, Maria Álvarez-Narciandi, Guillermo Cotton, Simon Yurduseven, Okan Queen’s University Belfast Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom University of Oviedo Group of Signal Theory and Communications Department of Electrical Electronic Communications and Systems Engineering Gijón33203 Spain

Computational imaging (CI) techniques realized by dynamic metasurface antennas (DMAs) offer significant advantages in the hardware design reducing the cost of such systems. In this paper, we present a physical layout based solely on DMA panels that can be used to synthesize electrically large apertures suitable for microwave imaging. Although hardware is significantly simplified, the challenge of using CI DMA-based systems presents itself in the signal processing layer. This is because in microwave CI the transfer function of the antennas is used to encode information from the imaged target scene and compress it into a single (or a few) channel(s). As a consequence, real-time Fourier-based image reconstruction algorithms cannot be directly applied because of the nature of the compressed back-scattered signals. In this work, we propose an algorithm that employs a decompression step such that the signal is converted to a suitable form for spatial frequency domain processing. The proposed technique mitigates the challenges of applying the algorithm in the near-field. Crucially, it also eliminates the need for a computationally expensive Stolt interpolation step, which can be a significant bottleneck for conventional Fourier-based image reconstruction algorithms. Numerical and experimental CI results are presented to validate the ability of the proposed system and algorithm to produce reconstructed images of good quality. The benefits of the proposed algorithm in terms of execution time and storage requirements are also explored in this work. © 2013 IEEE.

关键词： Image reconstruction

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Neural Network-assisted Interval Reachability for systems with Control Barrier Function-Based Safe Controllers

arXiv

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arXiv 2025年

作者： Ajeyemi, Damola Jafarpour, Saber Dall’Anese, Emiliano Division of Systems Engineering Boston University United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder United States Department of Electrical and Computer Engineering Division of Systems Engineering Boston University United States

Control Barrier Functions (CBFs) have been widely utilized in the design of optimization-based controllers and filters for dynamical systems to ensure forward invariance of a given set of safe states. While CBF-based controllers offer safety guarantees, they can compromise the performance of the system, leading to undesirable behaviors such as unbounded trajectories and emergence of locally stable spurious equilibria. Computing reachable sets for systems with CBF-based controllers is an effective approach for runtime performance and stability verification, and can potentially serve as a tool for trajectory re-planning. In this paper, we propose a computationally efficient interval reachability method for performance verification of systems with optimization-based controllers by: (i) approximating the optimization-based controller by a pre-trained neural network to avoid solving optimization problems repeatedly, and (ii) using mixed monotone theory to construct an embedding system that leverages state-of-the-art neural network verification algorithms for bounding the output of the neural network. Results in terms of closeness of solutions of trajectories of the system with the optimization-based controller and the neural network are derived. Using a single trajectory of the embedding system along with our closeness of solutions result, we obtain an over-approximation of the reachable set of the system with optimization-based controllers. Numerical results are presented to corroborate the technical findings. © 2025, CC BY.

关键词： Network embeddings

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Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2978-2995页

作者： Lu, Xiaozhen Xiao, Liang Xiao, Yilin Xiong, Zehui Liu, Zhe Zhang, Yanyong Zhuang, Weihua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Xiamen University Department of Information and Communication Engineering Xiamen361005 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Zhejiang Lab Hangzhou310027 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark. © 2004-2012 IEEE.

关键词： Computation offloading

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From Simulation to Reality with Random Noise

From Simulation to Reality with Random Noise

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Luo, Steven Lipkis, Rory López-Francos, Ignacio Vlastos, Pavlo Agogino, Adrian Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Intelligent Systems Division NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at relatively low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is not straightforward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult to perform. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network to classify camera position of an ego vehicle relative to a center line. Further models are trained with different types of noise progressively added to the data. In our experiments, we find that naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Learnable Residual-Based Latent Denoising in Semantic Communication

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IEEE Wireless Communications Letters 2025年第5期14卷 1376-1380页

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University School of Cyber and Engineering Shanghai200240 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. © 2012 IEEE.

关键词： Noise reduction Semantics Transformers Signal to noise ratio Iterative methods Iterative decoding Decoding Training Noise measurement Merging

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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ARUC-GAN: A CycleGAN-Based Attention Residual U-Net for Low-Dose CT Denoising in Smart Healthcare

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IEEE Access 2025年 13卷 72544-72557页

作者： Mansour, Mohammad Djenouri, Djamel Said, Lobna A. Latif, Shahid Maalel, Ahmed Abdelhadi, Ameer M. S. McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4L8 Canada University of the West of England School of Computing and Creative Technologies BristolBS16 1QY United Kingdom Giza12588 Egypt University of Sousse ISSAT Sousse RIADI Laboratory National School of Computer Science ENSI Sousse4003 Tunisia

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover, conventional denoising methods struggle with the non-uniform noise distributions in LDCT images and often require complex projection data, which limits their effectiveness and generalizability. Herein, we propose an AI-based denoising approach using an Attention Residual U-Net (ARU-Net) architecture integrated into the CycleGAN framework named Attention Residual U-Net CycleGAN (ARUC-GAN). The proposed framework outperforms state-of-the-art denoising models, such as RED-CNN, EDCNN, and CTformer, according to experimental evaluation on the Mayo Clinic abdominal CT dataset. The model exhibits a peak signal-to-noise ratio (PSNR) of 34.82 dB, a structural similarity index (SSIM) of 0.85, and an RMSE of 0.018. Furthermore, the model successfully maintains edge structures with an Edge Keeping Index (EKI) of 0.835. The visual examination validates ARUC-GAN’s superior texture and detail preservation. The results indicate that the suggested method has great promise as a smart health tool for improving diagnosis accuracy in LDCT scans. © 2013 IEEE.

关键词： Electronic health record

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