Federated vs. Centralized Large Language Models: Robustness to Label Flipping on GSM8K

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the robustness of federated large language models against label flipping attacks compare to centralized training when evaluated on reasoning benchmarks like GSM8K. Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating threats: \em training data privacy intrusion\ and \em training data poisoning\. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the robustness of federated large language models against label flipping attacks compare to centralized training when evaluated on reasoning benchmarks like GSM8K?

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