5 citations · 11 across the 11 of their papers we have counts for
4 papers · 1 filter
Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning
Danni Peng, Yuan Wang, Kangning Cai +6
In healthcare, federated learning (FL) is a widely adopted framework that enables privacy-preserving collaboration among medical institutions. With large foundation models (FMs) de…
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
Danni Peng, Yuan Wang, Huazhu Fu +4
Personalized federated learning (PFL) studies effective model personalization to address the data heterogeneity issue among clients in traditional federated learning (FL). Existing…
Confidence-Based Task Prediction in Continual Disease Classification Using Probability Distribution
Tanvi Verma, Lukas Schwemer, Mingrui Tan +3
Deep learning models are widely recognized for their effectiveness in identifying medical image findings in disease classification. However, their limitations become apparent in th…
History-Aware and Dynamic Client Contribution in Federated Learning
Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu +6
Federated Learning (FL) is a collaborative machine learning (ML) approach, where multiple clients participate in training an ML model without exposing their private data. Fair and…