Diversity-Driven Federated Averaging Enhances CodeLlama Zero-Shot Reasoning on MATH

Abstract

Abstract: This report synthesises findings from 6 peer-reviewed papers addressing the following research question: Does diversity-driven federated averaging improve the zero-shot reasoning capabilities of CodeLlama on the MATH benchmark when trained on heterogeneous client data distributions. Large Language Models (LLMs) have demonstrated impressive capabilities in autogenerating code based on natural language instructions provided by humans. We observed that in the microservice models of edge computing, the problem of deployment latency optimization can be. 11 claims were extracted from source literature; 10 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 Question

Does diversity-driven federated averaging improve the zero-shot reasoning capabilities of CodeLlama on the MATH benchmark when trained on heterogeneous client data distributions?

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