  Due to the strong analytical ability of big data, deep learning has been
widely applied to train the collected data in industrial IoT. However, for
privacy issues, traditional data-gathering centralized learning is not
applicable to industrial scenarios sensitive to training sets. Recently,
federated learning has received widespread attention, since it trains a model
by only relying on gradient aggregation without accessing training sets. But
existing researches reveal that the shared gradient still retains the sensitive
information of the training set. Even worse, a malicious aggregation server may
return forged aggregated gradients. In this paper, we propose the VFL,
verifiable federated learning with privacy-preserving for big data in
industrial IoT. Specifically, we use Lagrange interpolation to elaborately set
interpolation points for verifying the correctness of the aggregated gradients.
Compared with existing schemes, the verification overhead of VFL remains
constant regardless of the number of participants. Moreover, we employ the
blinding technology to protect the privacy of the gradients submitted by the
participants. If no more than n-2 of n participants collude with the
aggregation server, VFL could guarantee the encrypted gradients of other
participants not being inverted. Experimental evaluations corroborate the
practical performance of the presented VFL framework with high accuracy and
efficiency.
