federated learning 1foundation models 1heterogeneous modalities 1multimodal medical imaging 1privacy-preserving AI 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CV2026
Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging
Shengchao Chen, Ting Shu
Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', turns removing a hospital's,…
cs.CV2026
FM: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging
Shengchao Chen, Ting Shu
The paper introduces FM², a federated learning framework that trains a unified foundation model for heterogeneous multimodal medical images while preserving privacy, using dual Mix…
cs.CL2026
Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
Shengchao Chen, Ting Shu, Sufen Ren
Photonic crystal fiber (PCF) inverse design remains challenging because candidate geometries must satisfy coupled optical targets under expensive electromagnetic simulation. Existi…