Federated vs Centralized Code Model Inference Latency and Throughput Scaling

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the inference latency and token generation throughput of federatedly trained code models scale with client device diversity relative to centrally trained counterparts of similar size. Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference latency and token generation throughput of federatedly trained code models scale with client device diversity relative to centrally trained counterparts of similar size?

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