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cs.LG2025
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…
cs.LG2025★ 7 cited
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
Marios Aristodemou, Xiaolan Liu, Yuan Wang +3
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine…
cs.LG2025
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…