FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
This work addresses the problem of unrealistic evaluations in FedLLM research for practitioners and researchers, offering a practical benchmark to enable fair comparisons and reduce effort, though it is incremental as it builds on existing FedLLM paradigms.
The authors tackled the lack of realistic datasets and benchmarks for federated learning of large language models (FedLLM) by proposing FedLLM-Bench, which includes 8 training methods, 4 datasets, and 6 evaluation metrics, with client numbers ranging from 38 to 747, to provide a comprehensive testbed for the community.
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.