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cs.LG2025
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Kai Zhang, Yutong Dai, Hongyi Wang +3
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…
cs.LG2024
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning
Mengxin Zheng, Jiaqi Xue, Xun Chen +3
Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. Ho…