Summary of Fedllm-bench: Realistic Benchmarks For Federated Learning Of Large Language Models, by Rui Ye et al.
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
by Rui Ye, Rui Ge, Xinyu Zhu, Jingyi Chai, Yaxin Du, Yang Liu, Yanfeng Wang, Siheng Chen
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a comprehensive testbed for federated learning large language models (FedLLM) called FedLLM-Bench, addressing the lack of realistic datasets and benchmarks in the field. The authors develop 8 training methods, 4 training datasets, and 6 evaluation metrics to evaluate existing FL methods and provide empirical insights. The proposed testbed includes three datasets for federated instruction tuning and one dataset for federated preference alignment, with varying client numbers from 38 to 747. The datasets incorporate diverse properties such as language, quality, quantity, instruction, length, embedding, and preference, capturing real-world scenarios. The authors believe that FedLLM-Bench can reduce required efforts, provide a practical testbed, and promote fair comparisons in the FedLLM community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is like a team effort where multiple parties work together to train big language models without sharing their data directly. This has been super helpful for many applications! However, there’s a problem – we don’t have good datasets or benchmarks to test our methods on. The authors of this paper decided to fix that by creating a special package called FedLLM-Bench. It includes 8 ways to train models, 4 types of data, and 6 metrics to measure how well they do. They even created some sample datasets with different languages, qualities, and sizes to test their methods. This will help the team working on federated learning language models make better comparisons and learn from each other. |
Keywords
» Artificial intelligence » Alignment » Embedding » Federated learning » Instruction tuning
