Adaptive Sampling Strategies Reduce Latency and Memory in Federated LLM Deployment

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: What is the impact of adaptive sampling strategies on the inference latency and memory footprint of deployed personalized LLMs compared to random sampling in bandwidth-constrained federated networks. Abstract This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of adaptive sampling strategies on the inference latency and memory footprint of deployed personalized LLMs compared to random sampling in bandwidth-constrained federated networks?

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