FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models

TL;DR

FedLLM-Bench introduces realistic, diverse datasets and benchmarks for federated learning of large language models, enabling more accurate evaluation and comparison of methods in real-world scenarios.

Contribution

This work provides the first comprehensive, realistic benchmark suite for FedLLM, including datasets, evaluation metrics, and experimental insights, addressing the gap of artificial datasets.

Findings

Benchmarking reveals strengths and weaknesses of existing FL methods.

Multilingual collaboration improves model performance across languages.

Diverse datasets capture real-world properties for more meaningful evaluations.

Abstract

Federated learning has enabled multiple parties to collaboratively train large language models without directly sharing their data (FedLLM). Following this training paradigm, the community has put massive efforts from diverse aspects including framework, performance, and privacy. However, an unpleasant fact is that there are currently no realistic datasets and benchmarks for FedLLM and previous works all rely on artificially constructed datasets, failing to capture properties in real-world scenarios. Addressing this, we propose FedLLM-Bench, which involves 8 training methods, 4 training datasets, and 6 evaluation metrics, to offer a comprehensive testbed for the FedLLM community. FedLLM-Bench encompasses three datasets (e.g., user-annotated multilingual dataset) for federated instruction tuning and one dataset (e.g., user-annotated preference dataset) for federated preference alignment, whose scale of client number ranges from 38 to 747. Our datasets incorporate several representative diversities: language, quality, quantity, instruction, length, embedding, and preference, capturing properties in real-world scenarios. Based on FedLLM-Bench, we conduct experiments on all datasets to benchmark existing FL methods and provide empirical insights (e.g., multilingual collaboration). We believe that our FedLLM-Bench can benefit the FedLLM community by reducing required efforts, providing a practical testbed, and promoting fair comparisons. Code and datasets are available at https://github.com/rui-ye/FedLLM-Bench.