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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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TDID: Transparent and Efficient Decentralized Identity Management with Blockchain

TDID: Transparent and Efficient Decentralized Identity Manag...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Hao, Jiakun Gao, Jianbo Xiang, Peng Zhang, Jiashuo Chen, Ziming Hu, Hao Chen, Zhong School of Computer Science Peking University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China Nanjing University State Key Lab for Novel Software Technology Jiangsu Nanjing China

ISBN: (纸本)9798350337020

Decentralized identity (DID) is an identity management framework aiming to return the ownership of an identity to its corresponding user. Recent studies propose to store the identifiers of DID issuers and implement identity management systems based on blockchain. However, existing systems cannot avoid identity tampering and verifiable credential abuse of decentralized identities, which makes the identity management opaque. In this paper, we propose TDID, a Transparent and efficient Decentralized IDentity management system with blockchain. The key insight behind TDID is to manage the registration and authentication of DIDs via smart contracts, and design Structured Merkle Patricia Tree (SMPT) as an underlying data structure to store identity data on blockchain. The smart contract based processes can improve transparency of decentralized identity management, while the SMPT data structure can realize efficient storage of DID data. We implement and evaluate TDID on different identity management operations, and the experimental results show that TDID can achieve about 3.1 times for write operation and 6.3 times for read operation while improving the transparency of DID management. © 2023 IEEE.

关键词： Blockchain Data Structure Decentralized Identity Smart Contract

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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Towards better dynamic graph learning: new architecture and unified library 23

Towards better dynamic graph learning: new architecture and ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Le Yu Leilei Sun Bowen Du Weifeng Lv State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (i) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (ii) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://***/yule-BUAA/DyGLib.

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TrustFed: A Reliable Federated Learning Framework With Malicious-Attack Resistance

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Su, Hang Zhou, Jianhong Feng, Gang Niu, Xianhua Xihua University School of Computer and Software Engineering Chengdu China University of Electronic Science and Technology of China National Key Lab on Communications Chengdu China

As a key technology in 6G research, federated learning (FL) enables collaborative learning in resource-constrained edge networks while ensuring individual data privacy. However, traditional federated learning still has many security issues such as model poisoning attacks. The existing defense methods are not robust enough and will bring a lot of overhead to the system, which restricts the application of federated learning in resource-constrained edge networks. To address this issue, in this paper we propose a lightweight hierarchical audit based FL (HiAudit-FL) framework, with aim to enhance the reliability and security of the learning process with minimal system overhead. Model-audit and parameter-audit are alternatively executed to improve audit accuracy while reducing system overhead. Meanwhile, partial audit method is applied for the model audit stage. As the multi-round audit-waiting clients selections is rendered complex by the unpredictability and variability of environmental factors, we model the clients selection process as a partially observable Markov process and propose a diffusion based deep reinforcement learning algorithm to obtain an optimized client selection strategy to minimize audit overhead while ensuring audit accuracy. Simulation results demonstrate that HiAuditFL can effectively identify and handle potential malicious clients accurately, with small system overhead. © 2015 IEEE.

关键词： Federated learning

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Active perception for grasp detection via neural graspness field 24

Active perception for grasp detection via neural graspness f...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoxiang Ma Modi Shi Boyang Gao Di Huang State Key Laboratory of Complex and Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics

ISBN: (纸本)9798331314385

This paper tackles the challenge of active perception for robotic grasp detection in cluttered environments. Incomplete 3D geometry information can negatively affect the performance of learning-based grasp detection methods, and scanning the scene from multiple views introduces significant time costs. To achieve reliable grasping performance with efficient camera movement, we propose an active grasp detection framework based on the Neural Graspness Field (NGF), which models the scene incrementally and facilitates next-best-view planning. Constructed in real-time as the camera moves, the NGF effectively models the grasp distribution in 3D space by rendering graspness predictions from each view. For next-best-view planning, we aim to reduce the uncertainty of the NGF through a graspness inconsistency-guided policy, selecting views based on discrepancies between NGF outputs and a pre-trained graspness network. Additionally, we present a neural graspness sampling method that decodes graspness values from the NGF to improve grasp pose detection results. Extensive experiments on the GraspNet-1Billion benchmark demonstrate significant performance improvements compared to previous works. Real-world experiments show that our method achieves a superior trade-off between grasping performance and time costs. Code is available at https://***/mahaoxiang822/ActiveNGF.

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A Privacy Preserving Method for IoT Forensics

A Privacy Preserving Method for IoT Forensics

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Wenzheng Chen, Boxi Fu, Xiao Gu, Qing Shi, Jin Du, Xiaojiang Zhou, Xiaoyang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University School of Information Management Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Innovation Research Institute Nanjing China

ISBN: (纸本)9798350351255

The diversity of the Internet of Things (IoT) poses challenges to privacy protection, especially in the field of digital forensics. How to ensure that only the private information of the suspect is provided, and not the irrelevant information of other users is disclosed is crucial, especially when obtaining evidence in the complex IoT environment. To the best of our knowledge, there are few studies on protecting the privacy of irrelevant users in the IoT forensics. However, it is very important to ensure that the evidence does not violate the privacy of other users when collecting evidence, because it directly determines whether the evidence is legal and whether it can be admissible in court. In this paper, a new method based on data provenance graph is designed to solve the privacy protection problem of IoT forensics. The key idea of this method is to protect privacy by dividing multi-user information and protecting it from an encryption perspective. The method consists of three main phrases: distinguishing different users' data provenance graphs using traversal search, node abstraction, and hiding techniques, utilizing pseudo-random dual-key negotiation methods tailored for the scenario to enhance privacy protection for unrelated users, and employing identity authentication technology to facilitate better investigation and extraction of data provenance graph information of criminal accomplices in specific scenarios. Example proves that this method has practical significance and promising application prospects in protecting the privacy of unrelated users in IoT forensics while ensuring evidence accessibility in special criminal scenarios. © 2024 IEEE.

关键词： Differential privacy

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

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