2 citations · 3 across the 4 of their papers we have counts for
4 papers
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
Mengyao Du, Miao Zhang, Yuwen Pu +3
To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical…
DAP: Domain-aware Prompt Learning for Vision-and-Language Navigation
Ting Liu, Yue Hu, Wansen Wu +3
Following language instructions to navigate in unseen environments is a challenging task for autonomous embodied agents. With strong representation capabilities, pretrained vision-…
Asymmetrically Decentralized Federated Learning
Qinglun Li, Miao Zhang, Nan Yin +2
To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, whic…
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
Qinglun Li, Li Shen, Guanghao Li +2
To address the communication burden issues associated with federated learning (FL), decentralized federated learning (DFL) discards the central server and establishes a decentraliz…