  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.
