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.
翻译:通过利用深层次的学习技术,数据驱动方法取得了巨大成功,因为以工业信息网络(IIot)生成的数据迅速增加。然而,安全和隐私问题是许多敏感数据驱动的工业情景中数据提供者的障碍,如医疗保健和自动驾驶。许多联邦学习(FL)方法已经为IIOT应用程序与DNNS提出,由于数据不完备、质量低、数量不足、敏感度等原因,这些工程的数据使用率仍然很低。因此,我们提议为深创模型(DGM)采用基于环对面的分散化联合学习(RDFL)计划,其中DGM是解决上述数据可用性问题的有希望的解决办法。与现有的IIOT FL工程相比,我们的RDFL计划提供通信效率,并保持培训业绩,以提升目标IIT任务中的DGMS。在拟议的RDFL中设计了一个新型的FL表层图以及基于地图的同步化方法,以改善分散的FL性功能和带宽度。此外,InterII-DFS系统(IP-FS-S-S-S-S-Sirmalal-Serview State Statrial State Statrial Streal sal sal sal silmal)与FS-S-Syal smal smal silmal smal slipal smal smal bedalpal bedal bedal bedalpaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldal 和Systemaldaldaldal 和FSmal 和FSmaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldal