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20232026
most citedFeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

4 citations · 6 across the 8 of their papers we have counts for

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Showing 2024Show all

2 papers · 1 filter

cs.NI2024

Retrieval-augmented Generation for GenAI-enabled Semantic Communications

Shunpu Tang, Ruichen Zhang, Yuxuan Yan +4

Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communicatio…

cs.LG2024★ 4 cited

FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

Yuxuan Yan, Qianqian Yang, Shunpu Tang +1

Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data i…