Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Madan, Oshail; Vidushi; Shoran, Preety Title A name given to the resource AI-Based Security and Privacy Solutions for Edge Computing Using Federated Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Computational Intelligence;Volume;1267;pp.301-323 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-16711-8_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-16711-8_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038040715?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038040715?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Madan O., Christ University, Bengaluru, India; Vidushi, Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India Description An account of the resource Edge computing, which reduces latency and bandwidth usage by performing computations on data closer to where they are generated, is generating considerable interest due to the rapid growth of the Internet of Things (IoT) and real-time applications. However, this new architecture brings more security issues, such as cyber-attacks, unauthorized access, and data leakage. This architectural change improves performance, but it also increases the risk for data leakage, unauthorized access and cyberattacks. These distributed/architecturally decentralized scenarios are not something traditional cloud security models are suitable for. Federated learning (FL), a privacy-preserving setting for distributed learning, comes as a new solution which enables edge devices to collaboratively update their models without disclosing locally learned models. When integrated with AI, FL offers intelligent, flexible, and privacy-preserving security to edge ecosystems. This chapter investigates the interplay between edge computing, FL, and AI, providing a detailed analysis of possible future developments, risk mitigation strategies, and existing threats. This chapter studies the pioneering role of AI-supported federated systems in defending the future generation of edge networks through recent research and applied studies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Artificial intelligence; Cybersecurity; Decentralized architecture; Edge computing; Federated learning; IoT; Privacy-preserving security; Real-time applications; Risk mitigation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 1860949X; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online