  Federated learning (FL) represents a pivotal shift in machine learning (ML)
as it enables collaborative training of local ML models coordinated by a
central aggregator, all without the need to exchange local data. However, its
application on edge devices is hindered by limited computational capabilities
and data communication challenges, compounded by the inherent complexity of
Deep Learning (DL) models. Model pruning is identified as a key technique for
compressing DL models on devices with limited resources. Nonetheless,
conventional pruning techniques typically rely on manually crafted heuristics
and demand human expertise to achieve a balance between model size, speed, and
accuracy, often resulting in sub-optimal solutions.
  In this study, we introduce an automated federated learning approach
utilizing informed pruning, called AutoFLIP, which dynamically prunes and
compresses DL models within both the local clients and the global server. It
leverages a federated loss exploration phase to investigate model gradient
behavior across diverse datasets and losses, providing insights into parameter
significance. Our experiments showcase notable enhancements in scenarios with
strong non-IID data, underscoring AutoFLIP's capacity to tackle computational
constraints and achieve superior global convergence.
