  Federated Learning (FL) is an emerging paradigm through which decentralized
devices can collaboratively train a common model. However, a serious concern is
the leakage of privacy from exchanged gradient information between clients and
the parameter server (PS) in FL. To protect gradient information, clients can
adopt differential privacy (DP) to add additional noises and distort original
gradients before they are uploaded to the PS. Nevertheless, the model accuracy
will be significantly impaired by DP noises, making DP impracticable in real
systems. In this work, we propose a novel Noise Information Secretly Sharing
(NISS) algorithm to alleviate the disturbance of DP noises by sharing negated
noises among clients. We theoretically prove that: 1) If clients are
trustworthy, DP noises can be perfectly offset on the PS; 2) Clients can easily
distort negated DP noises to protect themselves in case that other clients are
not totally trustworthy, though the cost lowers model accuracy. NISS is
particularly applicable for FL across multiple IoT (Internet of Things)
systems, in which all IoT devices need to collaboratively train a model. To
verify the effectiveness and the superiority of the NISS algorithm, we conduct
experiments with the MNIST and CIFAR-10 datasets. The experiment results verify
our analysis and demonstrate that NISS can improve model accuracy by 21% on
average and obtain better privacy protection if clients are trustworthy.
