关键词: 工业物联网, 隐私保护, 联邦学习, 非独立同分布数据

Abstract: The problems of data sharing and privacy protection of Industrial Internet of Things (IIOT) were well solved by multi-party collaborative training of federated learning without exchanging the local private data.But the model offset was mainly caused by partial model renewal when traditional federated learning faced the industrial data of Non Independent and Identically Distributed (Non-IID).The federated dynamic weighted learning was proposed to figure out the problem.The method was divided into two basic steps as partial renewal and global convergence.The federated distance algorithm was used to eliminate the influence of the local model with excessive offset in partial renewal phase;the dynamic weighted algorithm was proposed to assign greater training weights dynamically to the local data that was more beneficial to the overall model in global convergence phrase.The impact of local update to model offset and local model offset to global model were carefully considered in federated dynamic weighted learning method.The experimental results verified that the federated dynamic weighted learning method had good results in the face of industrial data of Non-IID.

Key words: Internet of things, privacy protection, federated learning, non-independent and identically distributed data