Diversity-Driven Client Selection in Federated Learning for CodeLlama-7B on HumanEval

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios. Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.