Quantized Large Language Model Inference Latency Scaling in Federated Edge Deployments

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection. Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection?

Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.