Data Quality Control in Federated Instruction-tuning of Large Language
Models
Data Quality Control in Federated Instruction-tuning of Large Language
Models
By leveraging massively distributed data, federated learning (FL) enables collaborative instruction tuning of large language models (LLMs) in a privacy-preserving way. While FL effectively expands the data quantity, the issue of data quality remains under-explored in the current literature on FL for LLMs. To address this gap, we propose a …