Adaptive Client Sampling by Update Norm in Federated Vision-Language Model Convergence

Abstract

Abstract: This report synthesises findings from 6 peer-reviewed papers addressing the following research question: How does the use of adaptive client sampling based on update norm magnitude in federated learning impact the convergence speed and accuracy of multimodal vision-language models on the VQA-v2. It is well understood that client-master communication can be a primary bottleneck in Federated Learning. In this work, we address this issue with a novel client subsampling scheme, where we restrict the number of clients allowed to communicate their updates back to the master. 17 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the use of adaptive client sampling based on update norm magnitude in federated learning impact the convergence speed and accuracy of multimodal vision-language models on the VQA-v2 benchmark compared to uniform client sampling?

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