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Author

T.Vengatesh

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Open access Jul 2026

CONDITION NUMBER-AWARE PRUNING: PRESERVING MATHEMATICAL STABILITY IN SPARSE NEURAL NETWORKS

This paper proposes a condition number-aware pruning framework that explicitly preserves mathematical stability during the pruning process, and significantly improves both standard accuracy and adversarial robustness compared to conventional pruning methods.

Jeromy R, K Bhavani, K.Mohana Lakshmi et al. · 0 citations
Open access Jul 2026

DISTRIBUTED INTELLIGENCE AT THE EDGE: A MATHEMATICAL FRAMEWORK FOR DECENTRALIZED LEARNING IN IOT NETWORKS

A novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality is proposed, offering a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.

N.Durga, A. Mary Posonia, Selvakumar et al. · 0 citations