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Gradient sparsification for efficient wireless federated learning with differential privacy

Science China(information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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Encrypted Malicious Traffic Detection Based on Albert and Deep Learning

SSRN

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SSRN 2024年

作者： Zang, Xiaodong Wang, Tongliang Zhang, Xinchang Gong, Jian Gao, Peng Zhang, Guowei School of Cyber Science and Engineering Qufu Normal University Qufu China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

The focus on privacy protection has brought much-encrypted network traffic. However, attackers always abuse traffic encryption to conceal malicious behaviors. Although researchers have proposed several enlightening detection methods, they must enhance the generalization ability or improve detection performance. Our inspiration is that the packet header fields, as do the underlying grammatical rules for constructing sentences, have a strict order. We consider the original packet as text and devise a robust approach with natural language processing and a deep learning model to improve the generalization ability and detection performance. We capture the critical keywords as characteristic representations of the traffic and design an adaptive domain generalization algorithm with a new loss function. It is robust against various datasets and can augment the minority malicious samples to balance the data. Simultaneously, we design an efficient feature selection algorithm, which obtains an optimal feature subset and reduces feature dimensions by 75.3%. We conducted extensive experiments with two open-source datasets and the actual Internet backbone traffic(CERNET). Experimental results demonstrate our proposal's efficiency and robustness, outperforming state-of-the-art detection schemes. Specifically, the accuracy rate of our work can improve by at most 22.8% and achieve the average detection latency of 0.73s in the backbone. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Enhancing GNN-based CQA Spam Detection: Question-Answer-Pair Perspective with Supervised Neighbor Selection

Enhancing GNN-based CQA Spam Detection: Question-Answer-Pair...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Chenyang Wei Xi Luo Lu Zhang Gaofeng He Haiting Zhu School of Computer Science Nanjing Audit University Nanjing China Key Laboratory of Computer Network Information Integration (Southeast University) Ministry of Education School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Community question answering (CQA) portals have become very popular platforms attracting numerous participants to share and acquire knowledge and information on the Internet. However, many malicious users post suggestive questions and deceptive answers to promote a target (product or service), which can extremely distort the users' decision, and make the CQA environment less credible. Although various methods have been proposed to detect the fraud content from CQA, most of them detect deceptive questions and answers separately, thus suffer the problem of interactive information missing. In this paper, we take the Question-Answer-Pair (QAP) as the detecting target and propose a GNN-based method to detect the spam activities in the CQA. Specifically, we also design a label-aware neighbor selection module based on supervised learning to select more appropriate neighbors for attributes aggregating in G NN. Extensive experiments are conducted in a real-world dataset and results demonstrate the advantage and effectiveness of the proposed model for CQA spam detection.

关键词： Cloud computing Supervised learning Artificial neural networks Big Data Question answering (information retrieval) Graph neural networks Data models Fraud Portals

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Disambiguated attention embedding for multi-instance partial-label learning 23

Disambiguated attention embedding for multi-instance partial...

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

作者： Wei Tang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Information and Physical Sciences The University of Newcastle Callaghan NSW Australia

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.

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Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification

arXiv

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arXiv 2024年

作者： Liu, Xin Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Lab. of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW2308 Australia

Although attention-based multi-instance learning algorithms have achieved impressive performances on slide-level whole slide image (WSI) classification tasks, they are prone to mistakenly focus on irrelevant patterns such as staining conditions and tissue morphology, leading to incorrect patch-level predictions and unreliable interpretability. Moreover, these attention-based MIL algorithms tend to focus on salient instances and struggle to recognize hard-to-classify instances. In this paper, we first demonstrate that attention-based WSI classification methods do not adhere to the standard MIL assumptions. From the standard MIL assumptions, we propose a surprisingly simple yet effective instance-based MIL method for WSI classification (FocusMIL) based on max-pooling and forward amortized variational inference. We argue that synergizing the standard MIL assumption with variational inference encourages the model to focus on tumour morphology instead of spurious correlations. Our experimental evaluations show that FocusMIL significantly outperforms the baselines in patch-level classification tasks on the Camelyon16 and TCGA-NSCLC benchmarks. Visualization results show that our method also achieves better classification boundaries for identifying hard instances and mitigates the effect of spurious correlations between bags and labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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ACDNet: Attention-guided Collaborative Decision network for Effective Medication Recommendation

arXiv

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arXiv 2023年

作者： Mi, Jiacong Zu, Yi Wang, Zhuoyuan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient’s medication records and specific medicines. Therefore, an Attention-guided Collaborative Decision network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios. © 2023, CC BY.

关键词： Data mining

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An improved Schnorr-based multi-signature scheme with application to blockchain 3

An improved Schnorr-based multi-signature scheme with applic...

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3rd IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2021

作者： Li, Chunlan Wu, Yulong Yu, Feng Computer Science and Engineering School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781665425186

A multi-signature scheme allows a group of signers to jointly generate a signature on a common message, which can be verified by anyone, making the signature more compact. Multi-signature has the advantage of fast verification, and it helps reduce bandwidth and the cost of validation and storage. Recently, the multi-signature has drawn much attention because its advantages are desirable for blockchain and cryptocurrency. However, the traditional Schnorr-based multisignature scheme suffers from an excessive number of communications. This paper proposes an improved Schnorr-based multi-signature scheme ASMS, which reduces one round of communication in a traditional scheme, supporting public key aggregation. Thus, it is suitable for e-business and e-government affairs scenarios. As an application, we implemented our solution in Fabric, an enterprise blockchain platform, using chaincode technology. © 2021 IEEE

关键词： Blockchain

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Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model

arXiv

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arXiv 2024年

作者： Shi, Jiang-Xin Zhang, Chi Wei, Tong Li, Yu-Feng National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University China National Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University China

Pre-trained vision-language models like CLIP have shown powerful zero-shot inference ability via image-text matching and prove to be strong few-shot learners in various downstream tasks. However, in real-world scenarios, adapting CLIP to downstream tasks may encounter the following challenges: 1) data may exhibit long-tailed data distributions and might not have abundant samples for all the classes;2) There might be emerging tasks with new classes that contain no samples at all. To overcome them, we propose a novel framework to achieve efficient and long-tailed generalization, which can be termed as Candle. During the training process, we propose compensating logit-adjusted loss to encourage large margins of prototypes and alleviate imbalance both within the base classes and between the base and new classes. For efficient adaptation, we treat the CLIP model as a black box and leverage the extracted features to obtain visual and textual prototypes for prediction. To make full use of multi-modal information, we also propose cross-modal attention to enrich the features from both modalities. For effective generalization, we introduce virtual prototypes for new classes to make up for their lack of training images. Candle achieves state-of-the-art performance over extensive experiments on 11 diverse datasets while substantially reducing the training time, demonstrating the superiority of our approach. The source code is available at https://***/shijxcs/Candle. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Disease Diagnosis with Cost-Sensitive Grouped Features Based on Deep Reinforcement Learning

Disease Diagnosis with Cost-Sensitive Grouped Features Based...

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International Joint Conference on Neural networks (IJCNN)

作者： Yikemaiti Sataer Zhiqiang Gao Xuelian Li School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Foreign Studies Nanjing University of Posts and Telecommunications

Traditional machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, in disease diagnosis, acquiring features requires a certain cost because there are many concerns, such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration. Moreover, some medical examination items are grouped in the real world, such as lab tests that return multiple measurements, resulting in naturally grouped features. In this work, to consider features that come in groups, we propose a novel feature selection method for disease diagnosis with cost-sensitive grouped features based on deep reinforcement learning (RL). We optimize the objective which is a trade-off between the cost of grouped features and the diagnostic accuracy, by training deep RL models. In addition, the approach is flexible, as it can be improved with new deep reinforcement learning algorithms. We evaluate the effectiveness of the proposed method on four publicly available datasets. Compared with the existing competitive feature selection methods, the proposed method can achieve higher learning accuracy with lower feature costs. This paper takes disease diagnosis as an example, and the proposed method is universal for problems with grouped features of different domains. (The source code is available at https://***/r/DRL-CSGF- Y821).

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