The embodiment of the invention relates to the technical field of
federated learning and
information security, in particular to a federated multi-target scheduling method and
system for industrial control
crowdsourcing testing, which is characterized in that sensitive data such as ability, experience and the like of a tester are only reserved at a local terminal by constructing a framework combining local
feature coding and federated model training, so that the
test efficiency is improved, and the
test efficiency is improved. Outward transmission is not carried out; only the model update quantity after the privacy enhancement
processing such as gradient
cutting and
differential privacy noise injection is uploaded to the federated matching learning
server, and only the model update quantity after the privacy enhancement
processing is uploaded to the
federated learning server, so that the leakage risk of concentrated data storage is avoided from the source, the requirements of privacy regulations such as GDPR and the like are completely met, and the reliability of the
system is improved. Compared with traditional privacy technologies such as
homomorphic encryption, the method has the advantages that the calculation and communication overhead is greatly reduced, the design of
local matching degree calculation is matched, the task matching response speed is greatly increased, and the real-time requirement of
industrial control system testing is met.