ACM Computing Surveys最新文献_第5页

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-31 DOI: 10.1145/3727643

Md Palash Uddin, Yong Xiang, Mahmudul Hasan, Jun Bai, Yao Zhao, Longxiang Gao

{"title":"A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions","authors":"Md Palash Uddin, Yong Xiang, Mahmudul Hasan, Jun Bai, Yao Zhao, Longxiang Gao","doi":"10.1145/3727643","DOIUrl":"https://doi.org/10.1145/3727643","url":null,"abstract":"Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Systematic Literature Review of Secure Instant Messaging Applications from a Digital Forensics Perspective

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-31 DOI: 10.1145/3727641

Abdur Rahman Onik, Joseph Brown, Clinton Walker, Ibrahim Baggili

{"title":"A Systematic Literature Review of Secure Instant Messaging Applications from a Digital Forensics Perspective","authors":"Abdur Rahman Onik, Joseph Brown, Clinton Walker, Ibrahim Baggili","doi":"10.1145/3727641","DOIUrl":"https://doi.org/10.1145/3727641","url":null,"abstract":"The use of instant messengers in organized crime has made retrieving digital evidence from these platforms crucial in investigations. However, the wide implementation of end-to-end encryption (e2ee) has made forensic analysis challenging since all data and multimedia stored in these platforms are encrypted. To retrieve evidence, forensic examiners rely on artifacts produced by secure messaging applications. Therefore, identifying the artifacts that can be gathered from secure messaging applications is crucial during emergency analysis, as law enforcement agencies monitor these platforms. This literature review aims to provide a comprehensive understanding of artifact retrieval and forensic analysis trends by summarizing studies conducted since inception on popular secure messaging applications, including WhatsApp, Signal, Telegram, Wickr, and Threema. The review covers artifact retrieval, forensic methodologies, and tools. The review findings are significant to the cybersecurity and research communities, as well as the users of these applications. Forensic investigators can leverage the information during their investigations, cybersecurity practitioners can identify weaknesses in implementation to develop better security policies, and users can make informed decisions regarding their privacy.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cryptographic Primitives in Script-based and Scriptless Payment Channel Networks: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-26 DOI: 10.1145/3725846

Xiuyuan Chen, Junzuo Lai, Chao Lin, Xinyi Huang, Debiao He

{"title":"Cryptographic Primitives in Script-based and Scriptless Payment Channel Networks: A Survey","authors":"Xiuyuan Chen, Junzuo Lai, Chao Lin, Xinyi Huang, Debiao He","doi":"10.1145/3725846","DOIUrl":"https://doi.org/10.1145/3725846","url":null,"abstract":"Payment Channel Networks (PCNs), pivotal for blockchain scalability, facilitate multiple off-chain payments between any two users. They utilize scripts to define and execute payment conditions in various blockchains, but this poses privacy, efficiency, and compatibility challenges. To overcome these, scriptless cleverly embeds payment conditions into digital signatures instead of complex scripts. Cryptography effectively safeguards the construction and publication of transactions in script-based and scriptless PCNs. Although several surveys analyze PCN protocols, only a few discuss their underlying scripting languages and even none explore the cryptography involved. This survey is the first to comprehensively overview cryptography in PCNs from scripting perspectives, filling the existing knowledge void. Our analysis offers a complete picture of script-based and scriptless protocols and their coexistence. We then explore advanced cryptographic primitives in both categories, systematically studying these for the first time, and demonstrate their instantiations in atomic swaps. Finally, we research vast related surveys and provide a future-oriented outlook.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-adaptive Federated Learning in Internet of Things Systems: A Review 物联网系统中的自适应联合学习：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-22 DOI: 10.1145/3725527

Abdulaziz Aljohani, Omer Rana, Charith Perera

引用次数: 0

The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape 联邦反击战：联盟学习隐私攻击、防御、应用和政策环境调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-21 DOI: 10.1145/3724113

Joshua Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger Roth

{"title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","authors":"Joshua Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger Roth","doi":"10.1145/3724113","DOIUrl":"https://doi.org/10.1145/3724113","url":null,"abstract":"Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be ”reverse engineered” to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise does not hold. In this survey paper, we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which the privacy of ann FL client can be broken. We further dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL and conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"34 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143666462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-17 DOI: 10.1145/3724420

Xubin Wang, Zhiqing Tang, Jianxiong Guo, Tianhui Meng, Chenhao Wang, Tian Wang, Weijia Jia

{"title":"Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models","authors":"Xubin Wang, Zhiqing Tang, Jianxiong Guo, Tianhui Meng, Chenhao Wang, Tian Wang, Weijia Jia","doi":"10.1145/3724420","DOIUrl":"https://doi.org/10.1145/3724420","url":null,"abstract":"The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need for real-time data processing. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models. We define on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy. The survey is structured around key themes, including the fundamental concepts of AI models, application scenarios across various domains, and the technical challenges faced in edge environments. We also discuss optimization and implementation strategies, such as data preprocessing, model compression, and hardware acceleration, which are essential for effective deployment. Furthermore, we examine the impact of emerging technologies, including edge computing and foundation models, on the evolution of on-device AI models. By providing a structured overview of the challenges, solutions, and future directions, this survey aims to facilitate further research and application of on-device AI, ultimately contributing to the advancement of intelligent systems in everyday life.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"33 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Hardware-Based AES SBoxes: Area, Performance, and Security

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-16 DOI: 10.1145/3724114

Phaedra Sophia Curlin, Jeff Heiges, Calvin Chan, Tamara Silbergleit Lehman

引用次数: 0

UAV Operations Safety Assessment: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-16 DOI: 10.1145/3723871

Omid Asghari, Naghmeh Ivaki, Henrique Madeira

{"title":"UAV Operations Safety Assessment: A Systematic Literature Review","authors":"Omid Asghari, Naghmeh Ivaki, Henrique Madeira","doi":"10.1145/3723871","DOIUrl":"https://doi.org/10.1145/3723871","url":null,"abstract":"The significant increase in urban UAVs, due to their benefits and commercial potential, will increase drone density and collision risks. To manage this, Unmanned Aircraft Systems Traffic Management (UTM), European implementation of UTM (U-space), and Air Traffic Management (ATM) are being developed for safe integration with other air traffic. Nonetheless, thorough safety assessments remain essential for ensuring UAV operation safety. In this study, we conducted a two-phase systematic literature review. First, we analyzed existing reviews on UAV operation safety assessments. Second, we examined primary studies with the goal of identifying i) safety assessment approaches, ii) employed methods/techniques, iii) defined and utilized safety metrics, iv) common tools/simulators, and v) stages of safety assessment addressed by each technique in the reviewed studies. As a result, we categorized safety assessment approaches into five groups: 1) Model-based, 2) Analytical-based, 3) Data-driven, 4) Experimental-based, and 5) Hybrid approaches. We found that Monte Carlo simulation and Specific Operations Risk Assessment (SORA) are the most commonly used methods for safety assessment. We identified 42 metrics and classified them into four groups: 1) Collision, 2) Performance, 3) Communication, and 4) Reliability Metrics. Additionally, we identified ten tools/simulators used for safety assessment. Finally, we observed that Stage 5 (safety risk evaluation) of the safety assessment process is the most frequently covered in the studies reviewed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"1 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-13 DOI: 10.1145/3720539

Mang Ye, Wei Shen, Bo Du, Eduard Snezhko, Vassili Kovalev, Pong C. Yuen

{"title":"Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey","authors":"Mang Ye, Wei Shen, Bo Du, Eduard Snezhko, Vassili Kovalev, Pong C. Yuen","doi":"10.1145/3720539","DOIUrl":"https://doi.org/10.1145/3720539","url":null,"abstract":"Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models with partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://github.com/shentt67/VFL_Survey.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"34 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-12 DOI: 10.1145/3723158

Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris

{"title":"Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities","authors":"Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris","doi":"10.1145/3723158","DOIUrl":"https://doi.org/10.1145/3723158","url":null,"abstract":"A security operations centre (SOC) is a facility where teams of security professionals, supported by advanced technologies and processes, work together to monitor, detect, and respond to cybersecurity incidents. With advances in AI technology, most of the SOC functions are increasingly becoming AI-driven. Among these, real-time alert monitoring and triage is particularly important. Recent studies, by both industry and academia, have highlighted the problem of alert fatigue and burnout in SOC. Several solutions have been proposed in the literature and by the industry to address this problem. In this paper, we review the existing literature and industry solutions on alert fatigue mitigation through the lenses of automation, augmentation, and human-AI collaboration. Based on the review, we identify four major causes of alert fatigue in SOC. We also examine the shortcomings of existing solutions and propose several potential research directions leveraging AI. By providing a comprehensive analysis of the state-of-the-art approaches and their limitations, this study contributes to the existing literature in an important field of study. We anticipate that it will inspire new research directions for addressing alert fatigue not just in SOCs but across other Command and Control (C2) domains as well.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"68 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0