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.

FedMap 是一种新颖的方法，通过协作学习逐渐稀疏全局模型，从而提高联邦学习系统的通信效率，适用于对隐私至关重要的医疗和金融领域。它采用迭代的振幅剪枝方法训练全局模型，以减少通信开销，避免了参数重新激活问题，实现了稳定的性能表现。FedMap 在多样的设置、数据集、模型架构和超参数中进行了广泛评估，证明了在 IID 和非 IID 环境下的性能，并相较于基线方法，FedMap 能够达到更稳定的客户端模型性能，并在不降低准确性的情况下至少实现 80% 的剪枝。