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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on wireless communications Jg. 24; H. 1; S. 659 - 675
Hauptverfasser:	Sun, Haofeng, Tian, Hui, Ni, Wanli, Zheng, Jingheng, Niyato, Dusit, Zhang, Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!