BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Dublin BEGIN:DAYLIGHT DTSTART:20250330T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:IST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250922T105638Z UID:E932B1FA-B0E5-4A5B-A70E-77D8CF870885 DTSTART;TZID=Europe/Dublin:20250407T140000 DTEND;TZID=Europe/Dublin:20250407T150000 DESCRIPTION:Federated learning in wireless networks represents a significan t advancement in the field of distributed artificial intelligence (AI). Th is approach allows machine learning models to be trained directly on users ’ devices\, such as phones\, autonomous vehicles\, or drones\, without t he need to transfer locally collected data to a central server. From the s ecurity perspective\, federated learning can be used to detect cyberattack s in wireless networks. In this context\, we will discuss an initial solut ion that leverages distributed federated learning to detect attacks. Howev er\, federated learning can itself be prone to cyberattacks. Consequently\ , we will explore reliable unmanned aerial vehicle (UAV) client participat ion in federated learning under attack conditions. Together\, these studie s highlight the importance of the interplay between federated learning and security for wireless networks.\n\nSpeaker(s): \, Prof. Wael Jaafar\n\nBl dg: Hamilton Building\, Maxwell Theatre\, Trinity College Dublin\, Dublin\ , Dublin\, Ireland LOCATION:Bldg: Hamilton Building\, Maxwell Theatre\, Trinity College Dublin \, Dublin\, Dublin\, Ireland ORGANIZER:wangh15@tcd.ie SEQUENCE:44 SUMMARY:Technique Talk: Federated Learning in Wireless Networks: A Security Perspective URL;VALUE=URI:https://events.vtools.ieee.org/m/477399 X-ALT-DESC:Description: <br /><p>Federated learning in wireless networks re presents a significant advancement in the field of distributed artificial intelligence (AI). This approach allows machine learning models to be trai ned directly on users&rsquo\; devices\, such as phones\, autonomous vehicl es\, or drones\, without the need to transfer locally collected data to a central server. From the security perspective\, federated learning can be used to detect cyberattacks in wireless networks. In this context\, we wil l discuss an initial solution that leverages distributed federated learnin g to detect attacks. However\, federated learning can itself be prone to c yberattacks. Consequently\, we will explore reliable unmanned aerial vehic le (UAV) client participation in federated learning under attack condition s. Together\, these studies highlight the importance of the interplay betw een federated learning and security for wireless networks.</p> END:VEVENT END:VCALENDAR