2 papers
cs.LG2026
Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients
Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5
Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parame…
cs.LG2025
FedAPM: Federated Learning via ADMM with Partial Model Personalization
Shengkun Zhu, Feiteng Nie, Jinshan Zeng +6
In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, an…