Federated Low-Rank Adaptation for Large Models Fine-Tuning Over Wireless Networks

The emergence of large language models (LLMs) with multi-task generalization capabilities is expected to improve the performance of artificial intelligence (AI)-as-a-service provision in 6G networks. By fine-tuning LLMs, AI services can become more precise and tailored to the demands of different do...

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Vydané v:	IEEE transactions on wireless communications Ročník 24; číslo 1; s. 659 - 675
Hlavní autori:	Sun, Haofeng, Tian, Hui, Ni, Wanli, Zheng, Jingheng, Niyato, Dusit, Zhang, Ping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation Algorithms Array signal processing Artificial intelligence convergence analysis Federated learning large language model Large language models LoRa Low-rank adaption Machine learning Massive MIMO MIMO communication OFDM Optimization Orthogonal Frequency Division Multiplexing over-the-air computation Performance enhancement Performance evaluation Training Tuning Wireless networks Wireless sensor networks
ISSN:	1536-1276, 1558-2248
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