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Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Computers in Biology and Medicine 2024年 177卷 108646-108646页

作者： Kumar, Rajesh Bernard, Cobbinah M. Ullah, Aman Khan, Riaz Ullah Kumar, Jay Kulevome, Delanyo K.B. Yunbo, Rao Zeng, Shaoning University of Electronic Science and Technology of China Huzhou313001 China Institute for Big Data Analytics Dalhousie University Halifax NS Canada Department of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan Shangdong250200 China

Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model's quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enha

关键词： Tumors

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Weak Random Feature Method for Solving Partial Differential Equations

arXiv

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arXiv 2025年

作者： Kuvakin, Mikhail Mei, Zijian Chen, Jingrun Moscow Institute of Physics and Technology Russia School of Artificial Intelligence and Data Science Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China School of Mathematical Sciences Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Big Data & AI Research and Engineering Center Suzhou China

The random feature method (RFM) has demonstrated great potential in bridging traditional numerical methods and machine learning techniques for solving partial differential equations (PDEs). It retains the advantages of mesh-free approaches while achieving spectral accuracy for smooth solutions, without the need for iterative procedures. However, the implementation of RFM in the identification of weak solutions remains a subject of limited comprehension, despite crucial role of weak solutions in addressing numerous applied problems. While the direct application of RFM to problems without strong solutions is fraught with potential challenges, we propose an enhancement to the original random feature method that is specifically suited for finding weak solutions and is termed as Weak RFM. Essentially, Weak RFM reformulates the original RFM by adopting the weak form of the governing equations and constructing a new linear system through the use of carefully designed test functions, ensuring that the resulting solution satisfies the weak form by default. To rigorously evaluate the performance of the proposed method, we conduct extensive experiments on a variety of benchmark problems, including challenging three-dimensional cases, and compare its performance with state of the art machine learning-based approaches. The results demonstrate that Weak RFM achieves comparable or superior accuracy while significantly reducing computational time and memory consumption, highlighting its potential as a highly efficient and robust tool for finding weak solutions to various PDE problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Iterative methods

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Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control

Over-the-Air Federated Edge Learning with Error-Feedback One...

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International Conference on Computer and Communications (ICCC)

作者： Yuding Liu Dongzhu Liu Richeng Jin Guangxu Zhu Qingjiang Shi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Computing Science University of Glasgow Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. The existing one-bit digital over-the-air aggregation scheme features one-bit gradient quantization at edge devices and majority voting-based decoding at edge server, and therefore offers enhanced communication efficiency. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation, while the aggregation errors caused by channel fading in Air-FEEL further impair the performance. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) with an optimized power control policy. To this end, we first derive the impact of aggregation errors on the convergence of FEEL with EFOBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The analytical results and optimized power control policy are validated by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the vanilla one-bit quantization scheme without error feedback or optimized power control policy.

关键词： Performance evaluation Fading channels Training Quantization (signal) Wireless networks Atmospheric modeling Power control Channel estimation Training data Convergence

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Automated Mapping of Global 30-m Tidal Flats Using Time-Series Landsat Imagery:Algorithm and Products

Journal of Remote Sensing

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Journal of Remote Sensing 2023年第1期3卷 362-377页

作者： Xiao Zhang Liangyun Liu Jinqing Wang Tingting Zhao Wendi Liu Xidong Chen International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China College of Geomatics Xi’an University of Science and TechnologyXi’an 710054China North China University of Water Resources and Electric Power Zhengzhou 450046China

Tidal flats are an important part of coastal ecosystems and play an important role in shoreline protection and biodiversity *** many efforts have been made in tidal flat mapping,an accurate global tidal flat product covering all coasts globally is still lacking and urgently *** this study,a novel method is proposed for the automated mapping of global tidal flats at 30 m(GTF30)in 2020 based on the Google Earth Engine,which is also the first global tidal flat dataset covering the high latitudes(>60°N).Specifically,we first propose a new spectral index named the LTideI index through a sensitivity analysis,which is robust and can accurately capture low-tide ***,globally distributed training samples are automatically generated by combining multisource datasets and the spatiotemporal refinement ***,the global coasts are divided into 5885°×5°geographical tiles,and the local adaptive classification strategy is used to map tidal flats in each 5°×5°region by using multisourced training features and the derived globally distributed training *** statistical results show that the total global area of tidal flats is about 140,922.5 km2,with more than 75% distributed on 3 continents in the Northern Hemisphere,especially in Asia(approximately 43.1% of the total).Finally,the GTF30 tidal flat dataset is quantitatively assessed using 13,994 samples,yielding a good overall accuracy of 90.34%.Meanwhile,the intercomparisons with several existing tidal flat datasets indicate that the GTF30 products can greatly improve the mapping accuracy of tidal ***,the novel method can support the automated mapping of tidal flats,and the GTF30 dataset can provide scientific guidance and data support for protecting coastal ecosystems and supporting coastal economic and social *** GTF30 tidal flat dataset in 2020 is freely accessible via https://***/10.5281/zenodo.7936721.

关键词： tidal coastal Tidal

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Optimizing Wireless Coverage and Capacity with PPO-Based Adaptive Antenna Configuration

Optimizing Wireless Coverage and Capacity with PPO-Based Ada...

引用

IEEE International Conference on Communications (ICC)

作者： Yingshuo Gu Shuqi Chai Bowen Sun Yi Chen Qingjiang Shi School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Optimizing antenna parameters like azimuth, down-tilt, and power is crucial for coverage and capacity optimization (CCO) in next-generation wireless networks. However, traditional expert knowledge-based methods struggle to maintain optimal results when faced with changing environments. To address this, we propose a guided deep reinforcement learning (DRL) algorithm that learns a policy to dynamically adjust antenna parameters based on the evolving environment. Our approach employs proximal policy optimization-based DRL and integrates a problem-specific pretraining process using zero-order gradient descent. The pretrain policy serves as a guiding policy, enabling the agent to explore and discover high-reward regions, thus accel-erating the learning process. The performance of our solution is validated by numerical experiments conducted on a 5G simulation platform with real-world topological properties. The results show that our approach achieves significantly faster convergence and outperforms baseline methods in terms of CCO performance.

关键词： Heuristic algorithms Wireless networks Neural networks Knowledge based systems Deep reinforcement learning Numerical models Antennas

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ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification

arXiv

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arXiv 2025年

作者： Ma, Yi Wang, Shuai Liu, Tianchi Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Institute for Infocomm Research A*STAR 138632 Singapore Shenzhen Research Institute of Big data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China

In speaker verification, we use computational method to verify if an utterance matches the identity of an enrolled speaker. This task is similar to the manual task of forensic voice comparison, where linguistic analysis is combined with auditory measurements to compare and evaluate voice samples. Despite much success, we have yet to develop a speaker verification system that offers explainable results comparable to those from manual forensic voice comparison. A novel approach, Explainable Phonetic Trait-Oriented (ExPO) network, is proposed in this paper to introduce the speaker’s phonetic trait which describes the speaker’s characteristics at the phonetic level, resembling what forensic comparison does. ExPO not only generates utterance-level speaker embeddings but also allows for fine-grained analysis and visualization of phonetic traits, offering an explainable speaker verification process. Furthermore, we investigate phonetic traits from within-speaker and between-speaker variation perspectives to determine which trait is most effective for speaker verification, marking an important step towards explainable speaker verification. Our code is available at https://***/mmmmayi/ExPO. Copyright © 2025, The Authors. All rights reserved.

关键词： Linguistics

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Intent-Oriented Network Slicing with Hypergraphs

引用

IEEE Network 2024年

作者： Zou, Sai Liwang, Minhui Wu, Bing Wu, Wen Sun, Yanglong Ni, Wei Guizhou University College of Big Data and Information Engineering China Micro-nano and Intelligent Manufacturing Engineering Research Centre Ministry of Education China College of Electronic and Information Engineering China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems China Peng Cheng National Laboratory China Jimei University Navigation College China Data61 Business Unit CSIRO Australia

Establishing network slices in line with the intent of particular applications through closed-loop automation represents a key goal on designing future advanced networks. However, the lack of association between application intents and network services imposes challenges to accurately capture user preference and generate customized network slices. This article introduces Intent-Oriented Network Slicing (IONS) based on hypergraph theory to realize the customization of application intents, while maximizing the utilization of network resources. Specifically, IONS introduces a cutting-edge hypergraph framework, utilizing scenarios and patterns as the foundational elements of its hyperedges, which can be further enhanced by creating a dynamic adaptive attention mechanism tailored to the hypergraph's structure. Through this innovative approach, a sophisticated correlation model is established, linking application intents with network slicing strategies. This model significantly enhances the precision of user acquisition, optimizing network resources and user experience, while thus improving the customization of slice generation. Simulations demonstrate that the generated network slices can accurately reflect application intents. © 1986-2012 IEEE.

关键词： Correlation theory

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A Hybrid Marine Predator Algorithm for Thermal-aware Routing Scheme in Wireless Body Area Networks

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Journal of Bionic engineering 2023年第1期20卷 81-104页

作者： Tao Hai Jincheng Zhou Mohammad Masdari Haydar Abdulameer Marhoon School of Computer and Information Qiannan Normal University for NationalitiesDuyun558000GuizhouChina Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000GuizhouChina Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Universiti Teknologi MARA40450Shah AlamSelangorMalaysia Department of Computer Engineering Islamic Azad UniversityUrmia BranchUrmiaIran Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq

Thermal-aware routing protocols in WBANs consider temperature factors in the routing process for preventing overheating of the tissues surrounding the sensor ***,providing an energy-efficient and thermal-aware routing in WBANs is a challenging *** deal with this problem,this article presents a novel temperature-aware routing protocol that applies Mamdani-based Fuzzy Logic Controllers(FLCs)for selecting the next forwarding node in routing data *** FLCs apply five important input factors such as the priority of the packet,and sensor node's remaining energy,temperature,distance,and link path ***,a new hybrid version of the Marine Predator Algorithm(MPA),named MPAOA is presented by combining the exploration and exploitation phases of the MPA and Arithmetic Optimization Algorithm(AOA).This algorithm is effectively applied for selecting the best possible set of fuzzy rules for FLCs and tuning their fuzzy *** experiments conducted in the Castalia simulator exhibit that the proposed temperature and priority-aware routing scheme can outperform other well-known routing schemes such as LATOR,TTRP,TAEO,ATAR,and EOCC-TARA in terms of metrics such as sensor nodes lifetime,the average temperature of the sensor nodes,and the percentage of the packets routed through non-overheated ***,it is shown that the MPAOA outperforms other algorithms such as Bat Algorithm(BA),Genetic Algorithm(GA),AOA,and MPA regarding the specified metrics.

关键词： WBANs Multi-hop routing Temperature Priority Fuzzy logic Bio-inspiration

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