Federated Intelligence Over the Air: From Centralized to Collaborative Sensing
November 4 @ 12:00 pm - 1:00 pm
Join us for the fifth session of the exciting webinar series on “New Frontiers in Signal Processing in 6G Wireless Networks”, a collaboration between IEEE Signal Processing, IEEE Communications Society chapters in Ottawa, and IEEE ComSoC Young <a href="http://Professionals.
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Speaker(s): Prof. Hina Tabassum
Agenda:
The next generation of wireless networks will no longer be confined to moving bits — they will sense, communicate, and learn simultaneously. This convergence is anticipated to enable distributed intelligence across devices, unlocking new capabilities for real-time perception and decision-making in dynamic environments. In this talk, two complementary advances in federated signal processing will be presented. First, an over-the-air federated edge learning (OTA-FEEL) framework with integrated radar sensing will be discussed. By leveraging echoes from the environment, rather than treating them solely as interference, robust model aggregation will be maintained while ensuring high-quality sensing and communication performance. A joint scheduling 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–inspired algorithm for distributed target tracking, will be presented. By treating local log-likelihood functions as loss functions in a distributed optimization framework, FedTrack enables devices to collaboratively estimate a moving target’s position and velocity. This communication-efficient method closely approximates centralized 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 sense and learn collaboratively. Implications for autonomous systems, smart cities, and beyond will be discussed, with emphasis on the central role of signal processing innovations in realizing this <a href="http://vision.
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