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IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy. Accept & Close Skip to Main Content * IEEE.org * IEEE Xplore * IEEE-SA * IEEE Spectrum * More Sites SUBSCRIBE SUBSCRIBE Cart Create AccountPersonal Sign In * Browse * My Settings * Help Institutional Sign In Institutional Sign In AllBooksConferencesCoursesJournals & MagazinesStandardsAuthorsCitations ADVANCED SEARCH Journals & Magazines >IEEE Wireless Communications >Volume: 28 Issue: 5 DISPERSED FEDERATED LEARNING: VISION, TAXONOMY, AND FUTURE DIRECTIONS Publisher: IEEE Cite This PDF Latif U. Khan; Walid Saad; Zhu Han; Choong Seon Hong All Authors Sign In or Purchase to View Full Text * * * * * Alerts ALERTS Manage Content Alerts Add to Citation Alerts -------------------------------------------------------------------------------- Abstract Document Sections * Introduction * DFL: Fundamentals and Taxonomy * DDFL Framework Foriot Systems * Conclusions and Future Directions Authors Figures References Keywords More Like This * Download PDF * View References * * Request Permissions * Save to * Alerts Abstract:The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome th...View more Metadata Abstract: The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been significant recent interest in the concept of federated learning. Federated learning offers on-device machine learning without the need to transfer end-device data to a third party location. However, federated learning has robustness concerns because it might stop working due to a failure of the aggregation server (e.g., due to a malicious attack or physical defect). Furthermore, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel framework of dispersed federated learning (DFL) that is based on true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented. Published in: IEEE Wireless Communications ( Volume: 28, Issue: 5, October 2021) Page(s): 192 - 198 Date of Publication: 13 November 2021 ISSN Information: DOI: 10.1109/MWC.011.2100003 Publisher: IEEE Funding Agency: Contents -------------------------------------------------------------------------------- INTRODUCTION Recent years have revealed a significant rise in the number of Internet of Things (IoT) devices to enable various applications, such as smart health-care, augmented reality, industry 4.0, and autonomous driving cars, among others. These applications use emerging communication and computing technologies along with machine learning to offer smart services. In order to deploy machine learning in large-scale, heterogeneous systems such as the IoT, it is necessary to preserve the privacy of the data and reduce the communication overhead. As a result, centralized machine learning techniques may not be suitable. Instead, federated learning (FL) [1] is a distributed machine learning solution that can be amenable to deployment in an IoT. Although FL enables on-device machine learning, it faces a few challenges: • Traditional FL based on a centralized aggregation server might suffer from a malicious user attack or failure due to physical damage, which significantly degrades the performance of FL. The aggregation server can be attacked by an outsider that is not participating in the learning process, or one of the end-devices participating in the learning process. • A malicious aggregation server can infer the end-devices' sensitive information from their learning model parameters [2]. Therefore, there is a need to address the privacy leakage challenge of FL. • FL requires a significant amount of communication resources for the iterative exchange of learning model parameters between the massive number of devices and the aggregation server. Sign in to Continue Reading AUTHORS FIGURES REFERENCES KEYWORDS More Like This Work-in-progress: cloud-based machine learning for IoT devices with better privacy 2017 International Conference on Embedded Software (EMSOFT) Published: 2017 Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) Published: 2020 Show More REFERENCES References is not available for this document. 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IEEE ACCOUNT * Change Username/Password * Update Address PURCHASE DETAILS * Payment Options * Order History * View Purchased Documents PROFILE INFORMATION * Communications Preferences * Profession and Education * Technical Interests NEED HELP? * US & Canada: +1 800 678 4333 * Worldwide: +1 732 981 0060 * Contact & Support * About IEEE Xplore * Contact Us * Help * Accessibility * Terms of Use * Nondiscrimination Policy * Sitemap * Privacy & Opting Out of Cookies A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2021 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.