Federated Prompt Learning survey explores LLM training and privacy

This paper provides a comprehensive survey of Federated Prompt Learning (FPL), a method for training large language models (LLMs) in a decentralized manner. FPL addresses challenges like high computational costs, data centralization, and privacy concerns by enabling collaborative model training without raw data sharing. The survey examines FPL's motivations, characteristics, and technologies, comparing it to traditional federated learning and full-model fine-tuning. It also analyzes the trade-offs in performance, efficiency, and scalability, while highlighting ongoing security, privacy, and robustness issues and future research directions. AI

IMPACT Provides a structured overview of a privacy-preserving approach to LLM training, guiding future research in decentralized AI.

RANK_REASON The item is a survey paper on a specific machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• federated learning
• Federated Prompt Learning
• Hugging Face
• large-language models

• paper
• safety

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