Model pruning enables localized and efficient federated learning for yield forecasting and data sharing

Andy Li, Milan Markovic, Peter Edwards, Georgios Leontidis

Abstract: Highlights•We propose a new federated pruning learning method for yield forecasting.•Our method can be used to improve local inference performance.•The method reduces communication costs during training and model sizes.•Our method facilitates the on-edge implementation of ML models using sparse tensor.•The method is generalizable and works with different pruning policies and schedules.