  By leveraging deep learning based technologies, the data-driven based
approaches have reached great success with the rapid increase of data generated
of Industrial Indernet of Things(IIot). However, security and privacy concerns
are obstacles for data providers in many sensitive data-driven industrial
scenarios, such as healthcare and auto-driving. Many Federated Learning(FL)
approaches have been proposed with DNNs for IIoT applications, these works
still suffer from low usability of data due to data incompleteness, low
quality, insufficient quantity, sensitivity, etc. Therefore, we propose a
ring-topogy based decentralized federated learning(RDFL) scheme for Deep
Generative Models(DGMs), where DGMs is a promising solution for solving the
aforementioned data usability issues. Compare with existing IIoT FL works, our
RDFL schemes provides communication efficiency and maintain training
performance to boost DGMs in target IIoT tasks. A novel ring FL topology as
well as a map-reduce based synchronizing method are designed in the proposed
RDFL to improve decentralized FL performance and bandwidth utilization. In
addition, InterPlanetary File System(IPFS) is introduced to further improve
communication efficiency and FL security. Extensive experiments have been taken
to demonstate the superiority of RDFL with either independent and identically
distributed(IID) datasets or non-independent and identically
distributed(Non-IID) datasets.
