  Mobile edge computing (MEC) is an emerging technology to transform the
cloud-based computing services into the edge-based ones. Autonomous vehicular
network (AVNET), as one of the most promising applications of MEC, can feature
edge learning and communication techniques, improving the safety for autonomous
vehicles (AVs). This paper focuses on the edge learning in AVNET, where AVs at
the edge of the network share model parameters instead of data in a distributed
manner, and an aggregator (e.g., a base station) aggregates parameters from AVs
and at the end obtains a trained model. Despite promising, security issues,
such as data leakage, computing integrity invasion and fault connection in
existing edge learning cases are not considered fully. To the best of our
knowledge, there lacks an effective scheme simultaneously covering the
foregoing security issues. Therefore, we propose \textit{SecEL}, a
privacy-preserving, verifiable and fault-tolerant scheme for edge learning in
AVNET. First, we leverage the primitive of bivariate polynomial-based secret
sharing to encrypt model parameters by one-time padding. Second, we use
homomorphic authenticator based on message authentication code to support
verifiable computation. Third, we mitigate the computation failure problem
caused by fault connection. Last, we simulate and evaluate SecEL in terms of
time cost, throughput and classification accuracy. The experiment results
demonstrate the effectiveness of SecEL.
