Irene Tenison — accepted AI-conference papers

2026

AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks

Irene Tenison, Soumyajit Chatterjee, Fahim Kawsar, Mohammad Malekzadeh

CVPR 2026

To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited compute and memory resources. On-device retraining with a target dataset can facilitate such adaptations; however, it rem

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2024

Knowledge Distillation in Federated Learning: A Practical Guide

Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish

IJCAI 2024poster

Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits, i.e., model homogeneity, high commun…

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