Title: Federated Learning in the Generative AI Era — Workshop
Theme framing (subheader): Presented as part of TDAI’s Yearlong Theme: The Geometrization of AI
Date & Time: Friday, November 21, 2025 · 2:30–4:00 PM (ET)
Location: Pomerene Hall Room 350 (Project Zone)
Audience/Format: Interactive workshop · Registration required · Refreshments provided · Limited to the first 25 registrants
Register Here: https://forms.office.com/Pages/ResponsePage.aspx?id=NlYJ61IQlUiVKx_53x0RIQf01PrT38lJi-Io7FKiF39UOVJPUjlXQkMxMFFQT0hVNEFEMktUMURGMC4u
Workshop Overview
This interactive workshop will bring together faculty and students to discuss research directions related to federated learning and large-scale model adaptation. The session will include guided prompts, discussion, and short research reflections.
Featuring: Dr. Xueru Zhang (Ohio State University) and graduate student presenters
Moderator: Dr. Subhadeep Paul (Ohio State University)
Refreshments will be provided. Participation is limited to the first 25 registrants.
This workshop extends Dr. Joshi’s seminar discussion, offering an interactive opportunity for faculty and students to explore federated fine-tuning of LLMs and parameter-efficient adaptation methods in greater depth.
The session will feature a short overview by Dr. Xueru Zhang followed by graduate student presentations and a moderated discussion led by Dr. Subhadeep Paul. Participants will have the opportunity to exchange perspectives on data privacy, communication efficiency, and adaptive model design in federated environments.
Refreshments will be provided. Space is limited to the first 25 registrants.