  Federated Learning (FL) is a distributed machine learning approach that
enables training on decentralized data while preserving privacy. However, FL
systems often involve resource-constrained client devices with limited
computational power, memory, storage, and bandwidth. This paper introduces
FedMap, a novel method that aims to enhance the communication efficiency of FL
deployments by collaboratively learning an increasingly sparse global model
through iterative, unstructured pruning. Importantly, FedMap trains a global
model from scratch, unlike other methods reported in the literature, making it
ideal for privacy-critical use cases such as in the medical and finance
domains, where suitable pre-training data is often limited. FedMap adapts
iterative magnitude-based pruning to the FL setting, ensuring all clients prune
and refine the same subset of the global model parameters, therefore gradually
reducing the global model size and communication overhead. The iterative nature
of FedMap, forming subsequent models as subsets of predecessors, avoids
parameter reactivation issues seen in prior work, resulting in stable
performance. In this paper we provide an extensive evaluation of FedMap across
diverse settings, datasets, model architectures, and hyperparameters, assessing
performance in both IID and non-IID environments. Comparative analysis against
the baseline approach demonstrates FedMap's ability to achieve more stable
client model performance. For IID scenarios, FedMap achieves over $90$\%
pruning without significant performance degradation. In non-IID settings, it
achieves at least $~80$\% pruning while maintaining accuracy. FedMap offers a
promising solution to alleviate communication bottlenecks in FL systems while
retaining model accuracy.
