Federated Learning Framework Based on Data Value Evaluation in Industrial IoT

Abstract

With the continuous maturity and development of the big data technology system, deep learning has been widely used in the field of the Industrial Internet of Things. However, the traditional training model with centralized data is prone to the leakage of private information in the industry, such as facial information. In recent years, federated learning solves the problem of privacy leakage caused by data sharing by not sharing data and only contributing to local models. Federated learning does not share data, which also makes it impossible to evaluate the contribution of each client to the federated task. We propose a federated learning framework based on data value evaluation. In this method, under the premise of effectively completing the training task of federated learning and ensuring the privacy of client data, a data value evaluator is designed in the central server, and the model uploaded by the client is evaluated to obtain the corresponding selection probability as the model aggregation weight. Experimental results show that the proposed method improves the accuracy of the global model obtained by existing federated aggregation.