Federated Learning (FL) is a promising distributed method for edge-level
machine learning, particularly for privacysensitive applications such as those
in military and medical domains, where client data cannot be shared or
transferred to a cloud computing server. In many use-cases, communication cost
is a major challenge in FL due to its natural intensive network usage. Client
devices, such as smartphones or Internet of Things (IoT) nodes, have limited
resources in terms of energy, computation, and memory. To address these
hardware constraints, lightweight models and compression techniques such as
pruning and quantization are commonly adopted in centralised paradigms. In this
paper, we investigate the impact of compression techniques on FL for a typical
image classification task. Going further, we demonstrate that a straightforward
method can compresses messages up to 50% while having less than 1% of accuracy
loss, competing with state-of-the-art techniques.

本文研究压缩技术对典型图像分类任务的联邦学习的影响，并证明了一种简单的方法可以在保持不到 1% 准确率损失的同时压缩 50% 的消息，与最先进的技术相媲美。