AI ethics evidence index Search
Source-linked daily record
Evidence record 15880 · automatically gathered

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients' datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client select

Record details

Published: 3 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 4 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.

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

arXiv · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers

Frontiers in Artificial Intelligence · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Exploratory Integration of EEG Spectral Features and Gaze Variability for Mild Cognitive Impairment Discrimination

arXiv cs.HC · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

arXiv cs.CY · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

arXiv fairness query · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Deep learning for security-relevant event detection in visual data: a structured narrative review of the state of the art and future challenges

Artificial Intelligence Review · 31 July 2026

How to cite this record

ethics.ai (3 August 2026), “Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning,” evidence record 15880, https://ethics.ai/record/15880 (originally published by arXiv).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.