Federated LLM Code Generation Under Data Heterogeneity and Partial Participation

Abstract

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 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?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance