Optimizing Model Pruning in Decentralized Learning Networks with DFL-Trim

In recent decades, applications in environmental sustainability, education, and housekeeping have become increasingly distributed and sophisticated, leveraging a wide range of devices to perform complex tasks. While a large number of agents can reduce computation time, managing these distributed systems presents significant challenges due to resource constraints such as power consumption and storage. To address this, the literature has explored various model compression techniques, such as pruning, to optimize performance in distributed environments. In this paper, we propose DFL-Trim, a solution for trimming models in Decentralized Federated Learning (FL) that meets network constraints while maintaining satisfactory performance. We demonstrate how pruning can be implemented in decentralized settings, analyze its effect on bandwidth usage, and discuss the trade-offs between compression and model accuracy.