Hasnain Irshad Bhatti — accepted AI-conference papers

2026

PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusion

Humaira Kousar, Hasnain Irshad Bhatti, Jaekyun Moon

ICML 2026poster

Efficient data selection is crucial for enhancing the training efficiency of deep neural networks and minimizing annotation requirements. Traditional methods often face high computational costs, limiting their scalability and practical use. We introduce PruneFuse, a novel strategy that leverages pru…

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2023

EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning

Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, Younghyun Park, Humaira Kousar, Do-Yeon Kim, Jaekyun Moon

NeurIPS 2023poster

Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to share data. However, its broad adoption is hindered by the high communication costs of transmitting a large number of mode…

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