Scaling Federated Clients in Graph Learning with Prototype and Traditional Embeddings

Abstract

Abstract: This report synthesises findings from 2 peer-reviewed papers addressing the following research question: What is the effect of varying the number of federated clients on the convergence speed and inference latency of federated graph learning models using prototype-based embeddings versus traditional. Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inherently involves large-scale distributed graph processing, which closely aligns with the challenges. 10 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the effect of varying the number of federated clients on the convergence speed and inference latency of federated graph learning models using prototype-based embeddings versus traditional embeddings, evaluated using training loss and inference time on the Cora and Citeseer benchmarks?

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