基于联邦增量学习的工业物联网数据共享方法

Data sharing method of industrial internet of things based on federal incremental learning

针对工业物联网(IIOT)新增数据量大、工厂子端数据量不均衡的问题,提出了一种基于联邦增量学习的IIOT数据共享方法(FIL-IIOT)。首先,将行业联合模型下发到工厂子端作为本地初始模型;然后,提出联邦优选子端算法来动态调整参与子集;最后,通过联邦增量学习算法计算出工厂子端的增量加权,从而使新增状态数据与原行业联合模型快速融合。实验结果表明,在美国凯斯西储大学(CWRU)轴承故障数据集上,所提FIL-IIOT使轴承故障诊断精度达到93.15%,比联邦均值(FedAvg)算法和无增量公式的FIL-IIOT(FIL-IIOT-NI)方法分别提高了6.18个百分点和2.59个百分点,满足了基于工业增量数据的行业联合模型持续优化的需求。

In view of the large amount of new data in the Industrial Internet Of Things(IIOT)and the imbalance of data at the factory sub-ends,a data sharing method of IIOT based on Federal Incremental Learning(FIL-IIOT)was proposed.Firstly,the industry federation model was distributed to the factory sub-end as the local initial model.Then,the federal subend optimization algorithm was proposed to dynamically adjust the participating subset.Finally,the incremental weight of the factory sub-end was calculated through the federal incremental learning algorithm,thereby integrating the new state data with the original industry federation model quickly.Experimental results the Case Western Reserve University(CWRU)bearing failure dataset show that the proposed FIL-IIOT makes the accuracy of bearing fault diagnosis reached 93.15%,which is 6.18 percentage points and 2.59 percentage points higher than those of Federated Averaging(FedAvg)algorithm and FIL-IIOT of Non Increment(FIL-IIOT-NI)method,respectively.The proposed method meets the needs of continuous optimization of industry federation model based on industrial incremental data.