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Federated Learning (FL) is an emerging paradigm through which decentralizeddevices can collaboratively train a common model. However, a serious concern isthe leakage of privacy from exchanged gradient information between clients andthe parameter server (PS) in FL. To protect gradient information, clients canadopt differential privacy (DP) to add additional noises and distort originalgradients before they are uploaded to the PS. Nevertheless, the model accuracywill be significantly impaired by DP noises, making DP impracticable in realsystems. In this work, we propose a novel Noise Information Secretly Sharing(NISS) algorithm to alleviate the disturbance of DP noises by sharing negatednoises among clients. We theoretically prove that: 1) If clients aretrustworthy, DP noises can be perfectly offset on the PS; 2) Clients can easilydistort negated DP noises to protect themselves in case that other clients arenot totally trustworthy, though the cost lowers model accuracy. NISS isparticularly applicable for FL across multiple IoT (Internet of Things)systems, in which all IoT devices need to collaboratively train a model. Toverify the effectiveness and the superiority of the NISS algorithm, we conductexperiments with the MNIST and CIFAR-10 datasets. The experiment results verifyour analysis and demonstrate that NISS can improve model accuracy by 21% onaverage and obtain better privacy protection if clients are trustworthy.

Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning