Federated vs Centralized Code Model Inference Latency and Throughput Scaling

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 goal: 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?

Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.