Federated LLM Code Generation Under Data Heterogeneity and Partial Participation

Abstract

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of data heterogeneity under partial client participation on the code generation capabilities of federated LLMs as measured by HumanEval pass@k scores. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of data heterogeneity under partial client participation on the code generation capabilities of federated LLMs as measured by HumanEval pass@k scores? Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.