Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing

19 April 2023

Papers citing "Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing"

6 / 6 papers shown

Title
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning Tewodros Alemu Ayall Andy Li Matthew Beddows Milan Markovic Georgios Leontidis 31 0 0 25 Apr 2025
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 147 674 0 24 Jan 2021
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 243 1,452 0 18 Mar 2020
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,707 0 18 Mar 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 191 1,027 0 06 Mar 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 174 760 0 28 Sep 2019