Benchmarking Federated Learning for Large Language Models with FedLLM-Bench
What are the main challenges addressed by Federated Learning (FL) in training large language models (LLMs)?
Federated Learning (FL) addresses two main challenges in training large language models (LLMs): data privacy and the requirement for massive data collection and annotation efforts.
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Data Privacy: FL enables collaborative training of LLMs on decentralized data while preserving privacy. It allows multiple parties to train a shared global model without directly sharing their raw data1. This is particularly important in scenarios where data cannot be directly shared due to property and privacy issues.
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Data Collection and Annotation Efforts: Training LLMs centrally requires a significant amount of data collection and annotation, which can be costly for individual parties. FL offers a solution by enabling collaborative training on decentralized data, leveraging the data available across multiple parties.
By addressing these challenges, FL facilitates the training of LLMs in a more privacy-preserving and efficient manner.
What is FedLLM-Bench, and which institutions are involved in proposing this benchmark?
FedLLM-Bench is the first realistic benchmark for Federated Large Language Models (FedLLM). It is proposed by researchers from Shanghai Jiao Tong University, Tsinghua University, and Shanghai AI Laboratory. This benchmark offers a comprehensive testbed with four datasets: Fed-Aya (multilingual instruction tuning), Fed-WildChat (multi-turn chat instruction tuning), Fed-ChatbotIT (single-turn chat instruction tuning), and Fed-ChatbotPA (preference alignment). These datasets are naturally split by real-world user IDs across 38 to 747 clients, capturing realistic federated properties like cross-device data partitioning. The benchmark aims to facilitate method comparisons and exploration of new research directions in the field of FedLLM.
Why do current works on federated learning datasets fail to capture the complexities of real-world cross-user data?
Current works on federated learning datasets fail to capture the complexities of real-world cross-user data because they construct artificial FL datasets by partitioning centralized datasets. This approach does not accurately represent the diverse and complex properties of real-world cross-user data, which can include variations in languages, data quality, quantity, sequence lengths, and user preferences. As a result, these artificial datasets may not fully reflect the challenges and nuances present in real-world federated learning scenarios.