Federated Learning marks a turning point in the implementation of decentralized machine learning (especially deep learning) for wireless devices by protecting users' privacy and safeguarding raw data from third-party access. It assigns the learning process independently to each client. First, clients locally train a machine learning model based on local data. Next, clients transfer local updates of model weights and biases (training data) to a server. Then, the server aggregates updates (received from clients) to create a global learning model. However, the continuous transfer between clients and the server increases communication costs and is inefficient from a resource utilization perspective due to the large number of parameters (weights and biases) used by deep learning models. The cost of communication becomes a greater concern when the number of contributing clients and communication rounds increases. In this work, we propose a novel framework, FedZip, that significantly decreases the size of updates while transferring weights from the deep learning model between clients and their servers. FedZip implements Top-z sparsification, uses quantization with clustering, and implements compression with three different encoding methods. FedZip outperforms state-of-the-art compression frameworks and reaches compression rates up to 1085x, and preserves up to 99% of bandwidth and 99% of energy for clients during communication.

本研究提出了一种新的框架FedZip，其中利用 Top-Z 稀疏化、量化和压缩三种不同的编码方法，实现在减小客户端和服务器之间传输的权重大小的同时，兼顾保护用户隐私和保护数据的目的。FedZip 表现出超过现有压缩框架的性能，压缩比率高达1085x，即使在通信过程中也保留了客户端的99%带宽和99%能量。

FEDZIP: 用于通信高效联邦学习的压缩框架