5 papers
Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Chethana Prasad Kabgere, Shylaja SS
Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federated averaging assumes that a…
On the Geometric Coherence of Global Aggregation in Federated Graph Neural Networks
Chethana Prasad Kabgere, Shylaja SS
Federated learning over graph-structured data exposes a fundamental mismatch between standard aggregation mechanisms and the operator nature of graph neural networks (GNNs). While…
DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
Chethana Prasad Kabgere, Shylaja S S
Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. I…
Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
Chethana Prasad Kabgere, Sudarshan T S B
Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data priva…
Visual Categorization Across Minds and Models: Cognitive Analysis of Human Labeling and Neuro-Symbolic Integration
Chethana Prasad Kabgere
Understanding how humans and AI systems interpret ambiguous visual stimuli offers critical insight into the nature of perception, reasoning, and decision-making. This paper examine…