Federated learning (FL) is an important paradigm for training machine learning models on data that resides on multiple devices, without centralizing the data. This is particularly important for training large language models (LLMs), which often require massive amounts of data and are too large to train on a single device.
FedLLM-Bench
FedLLM-Bench is a benchmark suite for evaluating FL algorithms for LLMs. It includes a suite of tasks that are representative of the types of tasks that LLMs are used for, such as text classification, question answering, and language generation.
Evaluation Methodology
We evaluate a range of FL algorithms using FedLLM-Bench. The algorithms are evaluated on their performance on the benchmark tasks, as well as their training time and communication efficiency.
Results
We find that FL algorithms can achieve performance that is comparable to centralized training on the benchmark tasks. However, FL algorithms require more training time and communication rounds than centralized training.
Conclusion
Our results show that FL is a promising paradigm for training LLMs. FL algorithms can achieve performance that is comparable to centralized training, but require more training time and communication rounds.
Future Work
We plan to extend FedLLM-Bench to include more tasks and datasets. We also plan to investigate new FL algorithms that can improve the performance, training time, and communication efficiency of FL for LLMs.
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J.O. Schneppat
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