  Recently collaborative learning is widely applied to model sensitive data
generated in Industrial IoT (IIoT). It enables a large number of devices to
collectively train a global model by collaborating with a server while keeping
the datasets on their respective premises. However, existing approaches are
limited by high overheads and may also suffer from falsified aggregated results
returned by a malicious server. Hence, we propose a Scalable,
Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to
train linear and logistic regression models for IIoT. We aim to reduce burden
from resource-constrained IIoT devices and trust dependence on cloud by
introducing fog as a middleware. SPRITE employs threshold secret sharing to
guarantee privacy-preservation and robustness to IIoT device dropout whereas
verifiable additive homomorphic secret sharing to ensure verifiability during
model aggregation. We prove the security of SPRITE in an honest-but-curious
setting where the cloud is untrustworthy. We validate SPRITE to be scalable and
lightweight through theoretical overhead analysis and extensive testbed
experimentation on an IIoT use-case with two real-world industrial datasets.
For a large-scale industrial setup, SPRITE records 65% and 55% improved
performance over its competitor for linear and logistic regressions
respectively while reducing communication overhead for an IIoT device by 90%.
