Federated Learning with Diversity-Driven Client Selection Enhances Multimodal Model Generalization

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: To what extent does the federated learning setup with diversity-driven client selection improve the generalization of multimodal models (e.g., CLIP, ViLBERT) across unseen domains compared to. Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent does the federated learning setup with diversity-driven client selection improve the generalization of multimodal models (e.g., CLIP, ViLBERT) across unseen domains compared to centralized training on aggregated data, as measured by transfer learning performance on domain-shifted 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