  In federated learning (FL), a machine learning model is trained on multiple
nodes in a decentralized manner, while keeping the data local and not shared
with other nodes. However, FL requires the nodes to also send information on
the model parameters to a central server for aggregation. However, the
information sent from the nodes to the server may reveal some details about
each node's local data, thus raising privacy concerns. Furthermore, the
repetitive uplink transmission from the nodes to the server may result in a
communication overhead and network congestion. To address these two challenges,
in this paper, a novel two-bit aggregation algorithm is proposed with
guaranteed differential privacy and reduced uplink communication overhead.
Extensive experiments demonstrate that the proposed aggregation algorithm can
achieve the same performance as state-of-the-art approaches on datasets such as
MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100, while ensuring differential
privacy and improving communication efficiency.
