Non-IID Data Distribution Impacts on Federated Language Model Convergence and Accuracy

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does non-IID data distribution affect the convergence rate and final accuracy of federated language models on code generation benchmarks. The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data. However, FL struggles with a significant performance reduction and. 6 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does non-IID data distribution affect the convergence rate and final accuracy of federated language models on code generation benchmarks?

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