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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification 5

A Simple Semi-Supervised Joint Learning Framework for Few-sh...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Lu, Shaoshuai Chen, Long Wang, Wenjing Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

The lack of labeled data is the bottleneck restricting deep text classification algorithm. State-of-the-art for most existing deep text classification methods follow the two-step transfer learning paradigm: pre-training a large model on an auxiliary task, and then fine-tuning the model on a labeled data. Their shortcoming is the high cost of training. To reduce training costs as well as alleviate the need for labeled data, we present a novel simple Semi-Supervised Joint Learning (SSJL) framework for few-shot text classification that captures the rich text semantics from large user-tagged data (referred to as weakly-labeled data) with noisy labels while also learning correct category distributions in small labeled data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-labeled setting. Besides, an appropriate temperature hyper-parameter can improve model robustness under label noise. The experimental results on four real-world datasets show that our approach outperforms the other baseline methods. Moreover, SSJL significantly boosts the deep models' performance with only 0.5% (i.e. 32 samples) of the labeled data, showing its robustness in the data sparsity scenario. © 2022 ACM.

关键词： Semantics

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Elevating Predictions: Sculpting Flight Delay Predictions with Recursive Feature Elimination

Elevating Predictions: Sculpting Flight Delay Predictions wi...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Wei Wei Chenchen Yu Zhao Yang Fu Bo School of Computer Science and Engineering and the Shaanxi Key Laboratory for Network Computing and Security Xi'an University Of Technology Xi’an China School of Computer Science and Engineering Xi'an University Of Technology Xi’an China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

With the rapid development of the aviation industry and the significant growth of aviation data, how to use big data technology to accurately predict flight delays has become a problem that needs to be solved in the civil aviation transportation industry, and the application of big data models in the aviation field has also become crucial. To address this challenge, a new combinatorial forecasting method is proposed in this study. This method integrates random forest algorithm and recursive feature elimination technology to construct a comprehensive prediction model, aiming at improving the accuracy and efficiency of flight delay prediction. Experiments show that the proposed method performs better in the prediction accuracy and prediction error of flight delay data after the data is normalized to eliminate the influence of different dimensions and magnitudes on the model performance, which proves the effectiveness and practicability of the proposed method.

关键词： Computers Accuracy Transportation industry Predictive models Big Data Prediction algorithms Data models Delays Forecasting Random forests

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

引用

2023 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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Innovations in Machine Learning for Flight Delay Prediction: A Targeted Overview

Innovations in Machine Learning for Flight Delay Prediction:...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Artificial intelligence technology is increasingly being applied to air traffic flow forecasting. Based on the core data of Web of Science, this study uses bibliometrics and content review methods to investigate the current situation, methodology evaluation and future research direction of flight delay prediction. Initially, a basic statistical analysis was performed on all the papers. This was followed by topic co-occurrence and keyword co-occurrence analysis, which revealed that most of the research focused on three main categories: machine learning methods, deep learning techniques, and algorithmic optimization strategies. Then reviewing the content in these categories, further content analysis showed that machine learning holds significant potential for handling large and diverse datasets.

关键词： Adaptation models Analytical models Computational modeling Machine learning Predictive models Prediction algorithms Data models Real-time systems Delays Optimization

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A fine-grained petri model for SQL time-blind injection

A fine-grained petri model for SQL time-blind injection

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhang, Guiling Zhang, Yaling Wang, Yichuan Zhu, Lei Ji, Wenjiang Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665441582

With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security. © 2021 IEEE.

关键词： Petri nets

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A Trusted Privacy-Preserving Model for Cross-Chain Transactions Based on zk_SNARKs

A Trusted Privacy-Preserving Model for Cross-Chain Transacti...

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International Conference on networking and network Applications (NaNA)

作者： Yichuan Wang Jianhuan Tie Xinhong Hei Li Zhao Xiaohui Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China

Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transactions. In this paper, we propose a method of cross-chain transactions based on zk_SNARKs in a trusted environment for cross-chain transactions for privacy protection. Cross-chain transactions are performed through the features of zk_SNARKs such as efficiency, privacy, and verifiability. zk_SNARKs needs to generate trusted settings with the help of a third party during the transaction, and the process has serious privacy issues, and the private parameters generated during the transaction may be maliciously attacked and thus obtained by attackers. To address this problem, we use a hardware technology SGX for trusted computing to encrypt and store some private parameters generated by zk_SNARKs, thus ensuring the security of cross-chain transactions. The experimental results show that the privacy protection scheme using SGX for cross-chain transactions takes less time than the traditional transaction privacy protection, with a verification time of 1.2s for a number of 10 sidechains, and is more efficient than the traditional cross-chain transaction privacy protection.

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ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Model for Driving Safety

ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Mo...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Hei, Xinhong Zhang, Hao Ji, Wenjiang Wang, Yichuan Zhu, Lei Qiu, Yuan College of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665441582

Risk prediction is one of the most important tasks in assistant and automatic driving. In recent years, by the help of VANET and various sensors in the cars, the status of cars and roads can be collected in real time and used for data-driven based driving risk prediction. However, it is challenging to predict the driving risk due to the complex relationship between multiple environment factors like location, weather, time etc. Thus, a deep learning model ConvCatb was proposed in this paper, which improves the attention mechanism to the traditional Convolutional Neural networks, and combines the CatBoost algorithm to predict the current driving safety. The main idea is to emphasize the combination relationship between driving environment features through Non-local attention mechanism, and then use CatBoost to replace the softmax of CNN for classification. Finally, the experiment results show that the ConvCatb achieved superiorities in accuracy and F1-score, compared with existing schemes. © 2021 IEEE.

关键词： Automobile drivers

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Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths

Deep Reinforcement-Learning-Based Adaptive Traffic Signal Co...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Qi-Wei Han, Shi-Yuan Zhou, Jin Chen, Yue-Hui Yao, Kang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Shandong Provincial Network Security and Infomationization Technology Center Jinan250002 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics. © 2022 IEEE.

关键词： Reinforcement learning

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

Wrong-of-Thought: An Integrated Reasoning Framework with Mul...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China Yunnan University Kunming China

ISBN: (纸本)9798891761681

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024 Association for Computational Linguistics.

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