BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260414T201155Z UID:728D9F04-10FD-45E8-BC4D-480C61130743 DTSTART;TZID=America/Los_Angeles:20260504T110000 DTEND;TZID=America/Los_Angeles:20260504T120000 DESCRIPTION:Abstract: The next generation of wireless networks will no long er be confined to moving bits — they will sense\, communicate\, and lear n simultaneously. This convergence is anticipated to enable distributed in telligence across devices\, unlocking new capabilities for real-time perce ption and decision-making in dynamic environments. In this talk\, two comp lementary advances in federated signal processing will be presented. First \, an over-the-air federated edge learning (OTA-FEEL) framework with integ rated radar sensing will be discussed. By leveraging echoes from the envir onment\, rather than treating them solely as interference\, robust model a ggregation will be maintained while ensuring high-quality sensing and comm unication performance. A joint scheduling and beamforming design will be p resented\, supported by low-complexity optimization techniques\, to preser ve aggregation accuracy under realistic wireless conditions. Second\, FedT rack\, a novel federated learning–inspired algorithm for distributed tar get tracking\, will be presented. By treating local log-likelihood functio ns as loss functions in a distributed optimization framework\, FedTrack en ables devices to collaboratively estimate a moving target’s position and velocity. This communication-efficient method closely approximates centra lized maximum likelihood estimation\, achieving accuracy near the Cramér –Rao bound while reducing reliance on a central coordinator. Together\, these developments illustrate how federated intelligence over the air can transform 6G networks into systems that not only communicate but also sens e and learn collaboratively. Implications for autonomous systems\, smart c ities\, and beyond will be discussed\, with emphasis on the central role o f signal processing innovations in realizing this vision.\n\nRoom: MCLD 30 38\, Bldg: Hector J. MacLeod Building - MCLD\, 2356 Main Mall\, Vancouver\ , BC V6T 1Z4\, Vancouver\, British Columbia\, Canada\, Virtual: https://ev ents.vtools.ieee.org/m/552228 LOCATION:Room: MCLD 3038\, Bldg: Hector J. MacLeod Building - MCLD\, 2356 M ain Mall\, Vancouver\, BC V6T 1Z4\, Vancouver\, British Columbia\, Canada\ , Virtual: https://events.vtools.ieee.org/m/552228 ORGANIZER:benjamin.crockett@ieee.org SEQUENCE:39 SUMMARY:Distinguished Lecturer Tour: Federated Intelligence Over the Air: F rom Centralized to Collaborative Sensing URL;VALUE=URI:https://events.vtools.ieee.org/m/552228 X-ALT-DESC:Description: <br /><p><img src="https://events.vtools.ieee.org/v tools_ui/media/display/d230fbfe-62de-4b25-b478-d9e37ad2c63b" width="952" h eight="536"></p>\n<p><strong>Abstract: </strong><span lang="EN-US" style=" font-family: 'Aptos'\,sans-serif\; color: black\;">The next generation of wireless networks will no longer be confined to moving bits &mdash\; they will sense\, communicate\, and learn simultaneously. This convergence is a nticipated to enable distributed intelligence across devices\, unlocking n ew capabilities for real-time perception and decision-making in dynamic en vironments. In this talk\, two complementary advances in federated signal processing will be presented. First\, an over-the-air federated edge learn ing (OTA-FEEL) framework with integrated radar sensing will be discussed. By leveraging echoes from the environment\, rather than treating them sole ly as interference\, robust model aggregation will be maintained while ens uring high-quality sensing and communication performance. A joint scheduli ng and beamforming design will be presented\, supported by low-complexity optimization techniques\, to preserve aggregation accuracy under realistic wireless conditions. Second\, FedTrack\, a novel federated learning&ndash \;inspired algorithm for distributed target tracking\, will be presented. By treating local log-likelihood functions as loss functions in a distribu ted optimization framework\, FedTrack enables devices to collaboratively e stimate a moving target&rsquo\;s position and velocity. This communication -efficient method closely approximates centralized maximum likelihood esti mation\, achieving accuracy near the Cram&eacute\;r&ndash\;Rao bound while reducing reliance on a central coordinator. Together\, these developments illustrate how federated intelligence over the air&nbsp\;can transform 6G networks into systems that not only communicate but also sense and learn collaboratively. Implications for autonomous systems\, smart cities\, and beyond will be discussed\, with emphasis on the central role of signal pro cessing innovations&nbsp\;in realizing this vision.</span></p>\n<p><strong >&nbsp\;</strong></p> END:VEVENT END:VCALENDAR