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By leveraging deep learning based technologies, the data-driven basedapproaches have reached great success with the rapid increase of data generatedof Industrial Indernet of Things(IIot). However, security and privacy concernsare obstacles for data providers in many sensitive data-driven industrialscenarios, such as healthcare and auto-driving. Many Federated Learning(FL)approaches have been proposed with DNNs for IIoT applications, these worksstill suffer from low usability of data due to data incompleteness, lowquality, insufficient quantity, sensitivity, etc. Therefore, we propose aring-topogy based decentralized federated learning(RDFL) scheme for DeepGenerative Models(DGMs), where DGMs is a promising solution for solving theaforementioned data usability issues. Compare with existing IIoT FL works, ourRDFL schemes provides communication efficiency and maintain trainingperformance to boost DGMs in target IIoT tasks. A novel ring FL topology aswell as a map-reduce based synchronizing method are designed in the proposedRDFL to improve decentralized FL performance and bandwidth utilization. Inaddition, InterPlanetary File System(IPFS) is introduced to further improvecommunication efficiency and FL security. Extensive experiments have been takento demonstate the superiority of RDFL with either independent and identicallydistributed(IID) datasets or non-independent and identicallydistributed(Non-IID) datasets.

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent