Ioannis Schoinas;Anna Triantafyllou;Dimosthenis Ioannidis;Dimitrios Tzovaras;Anastasios Drosou;Konstantinos Votis;Thomas Lagkas;Vasileios Argyriou;Panagiotis Sarigiannidis
{"title":"联合学习:挑战、SoTA、性能改进和应用领域","authors":"Ioannis Schoinas;Anna Triantafyllou;Dimosthenis Ioannidis;Dimitrios Tzovaras;Anastasios Drosou;Konstantinos Votis;Thomas Lagkas;Vasileios Argyriou;Panagiotis Sarigiannidis","doi":"10.1109/OJCOMS.2024.3458088","DOIUrl":null,"url":null,"abstract":"Federated Learning has emerged as a revolutionary technology in Machine Learning (ML), enabling collaborative training of models in a distributed environment while ensuring privacy and security. This work discusses the topic of FL by providing insights into its various dimensions, perspectives, and components, leading to a comprehensive understanding of the technology. The survey begins by introducing the basic principles of FL and provides a high-level taxonomy of its methods. It continues by presenting application domains and associating challenges, categories and their applications. This mapping allows for an understanding of how particular challenges manifest in different contexts and applications. The main body delves into the various aspects of FL, including centralized and decentralized variants, methods for improving efficiency and effectiveness, and concerns regarding security, privacy, dynamic conditions, fairness, scalability and integration with other new technologies. Ultimately, the goal is to present recent advancements in these areas, along with new challenges and opportunities for future exploration. FL is poised to reshape the landscape of intelligent systems while promoting data privacy in decentralized and collaborative learning. Finally, this survey can serve as a reference point for methodological improvements as it highlights the strengths and weaknesses of existing approaches.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10677499","citationCount":"0","resultStr":"{\"title\":\"Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains\",\"authors\":\"Ioannis Schoinas;Anna Triantafyllou;Dimosthenis Ioannidis;Dimitrios Tzovaras;Anastasios Drosou;Konstantinos Votis;Thomas Lagkas;Vasileios Argyriou;Panagiotis Sarigiannidis\",\"doi\":\"10.1109/OJCOMS.2024.3458088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Federated Learning has emerged as a revolutionary technology in Machine Learning (ML), enabling collaborative training of models in a distributed environment while ensuring privacy and security. 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Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains
Federated Learning has emerged as a revolutionary technology in Machine Learning (ML), enabling collaborative training of models in a distributed environment while ensuring privacy and security. This work discusses the topic of FL by providing insights into its various dimensions, perspectives, and components, leading to a comprehensive understanding of the technology. The survey begins by introducing the basic principles of FL and provides a high-level taxonomy of its methods. It continues by presenting application domains and associating challenges, categories and their applications. This mapping allows for an understanding of how particular challenges manifest in different contexts and applications. The main body delves into the various aspects of FL, including centralized and decentralized variants, methods for improving efficiency and effectiveness, and concerns regarding security, privacy, dynamic conditions, fairness, scalability and integration with other new technologies. Ultimately, the goal is to present recent advancements in these areas, along with new challenges and opportunities for future exploration. FL is poised to reshape the landscape of intelligent systems while promoting data privacy in decentralized and collaborative learning. Finally, this survey can serve as a reference point for methodological improvements as it highlights the strengths and weaknesses of existing approaches.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.