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Evidence record 18024 · automatically gathered

FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence. To address this, we propose FedTVD, a novel FL algorithm that weights client contributions during aggregation by considering both data quality and quantity.

Record details

Published: 10 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 11 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (10 August 2026), “FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning,” evidence record 18024, https://ethics.ai/record/18024 (originally published by arXiv).

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