#fairness-ml News & Analysis

2 articles tagged with #fairness-ml. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · May 286/10

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Resource-Constrained Affect Modelling via Variance Regularisation Pruning

Researchers introduce Variance-Regularised Pruning (VR), a neural network pruning technique that reduces model size while maintaining robust performance across diverse users. The method balances computational efficiency with cross-participant stability in affective computing systems, achieving 80% sparsity without sacrificing reliability on the AGAIN emotion recognition dataset.

AINeutralarXiv – CS AI · Apr 206/10

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Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure

Researchers introduce Availability-Weighted Probabilistic Synchronous Parallel (AW-PSP), an improved federated learning algorithm that addresses bias in node sampling when device availability and data distribution are correlated. The technique uses dynamic probability adjustments, Markov-based failure prediction, and distributed metadata management to improve fairness and robustness in edge computing environments where devices frequently fail or become unavailable.