Pub Date : 2024-10-10DOI: 10.1109/TETC.2024.3474484
Enrique Mármol Campos;Aurora Gonzalez-Vidal;José L. Hernández-Ramos;Antonio Skarmeta
Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handle sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients’ weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.
{"title":"FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning","authors":"Enrique Mármol Campos;Aurora Gonzalez-Vidal;José L. Hernández-Ramos;Antonio Skarmeta","doi":"10.1109/TETC.2024.3474484","DOIUrl":"https://doi.org/10.1109/TETC.2024.3474484","url":null,"abstract":"Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handle sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients’ weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"48-67"},"PeriodicalIF":5.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1109/TETC.2024.3473911
Quy Vu Khanh;Abdellah Chehri;Van Anh Dang;Quy Nguyen Minh
Across all periods of human history, the importance attributed to health has remained a fundamental and significant facet. This statement holds greater validity within the present context. The pressing demand for healthcare solutions with real-time capabilities, affordability, and high precision is crucial in medical research and technology progress. In recent times, there has been a significant advancement in emerging technologies such as AI, IoT, blockchain, and edge computing. These breakthrough developments have led to the creation of various intelligent applications. Smart healthcare applications can be realized by combining robust AI detection and prediction capabilities with edge computing architecture, which offers low computing costs and latency. In this paper, we begin by conducting a literature review of AI-assisted EC-based smart healthcare applications from the past three years. Our goal is to identify gaps and barriers in this field. We propose a smart healthcare architecture model that integrates AI technology into the edge. Finally, we summarize the challenges and research directions associated with the proposed model.
{"title":"Federated Learning Approach for Collaborative and Secure Smart Healthcare Applications","authors":"Quy Vu Khanh;Abdellah Chehri;Van Anh Dang;Quy Nguyen Minh","doi":"10.1109/TETC.2024.3473911","DOIUrl":"https://doi.org/10.1109/TETC.2024.3473911","url":null,"abstract":"Across all periods of human history, the importance attributed to health has remained a fundamental and significant facet. This statement holds greater validity within the present context. The pressing demand for healthcare solutions with real-time capabilities, affordability, and high precision is crucial in medical research and technology progress. In recent times, there has been a significant advancement in emerging technologies such as AI, IoT, blockchain, and edge computing. These breakthrough developments have led to the creation of various intelligent applications. Smart healthcare applications can be realized by combining robust AI detection and prediction capabilities with edge computing architecture, which offers low computing costs and latency. In this paper, we begin by conducting a literature review of AI-assisted EC-based smart healthcare applications from the past three years. Our goal is to identify gaps and barriers in this field. We propose a smart healthcare architecture model that integrates AI technology into the edge. Finally, we summarize the challenges and research directions associated with the proposed model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"68-79"},"PeriodicalIF":5.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/TETC.2024.3449211
{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3449211","DOIUrl":"https://doi.org/10.1109/TETC.2024.3449211","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/TETC.2024.3447428
Yuan-Hao Chang;Paloma Díaz;Yunpeng Xiao
{"title":"Special Section on Emerging Social Computing","authors":"Yuan-Hao Chang;Paloma Díaz;Yunpeng Xiao","doi":"10.1109/TETC.2024.3447428","DOIUrl":"https://doi.org/10.1109/TETC.2024.3447428","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"686-687"},"PeriodicalIF":5.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1109/tetc.2024.3440932
Ramashish Gaurav, Duy Anh Do, Thinh Doan, Yang Yi
{"title":"DALTON - Deep Local Learning in SNNs via local Weights and Surrogate-Derivative Transfer","authors":"Ramashish Gaurav, Duy Anh Do, Thinh Doan, Yang Yi","doi":"10.1109/tetc.2024.3440932","DOIUrl":"https://doi.org/10.1109/tetc.2024.3440932","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"8 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}