Federated Learning Intellectual Capital Platform

In the era of artificial intelligence, trained neural network models have become new products of the information age. Most of machine learning strategies currently used to train neural networks are supervised learning, and thus, training data with labels become new intellectual capital (IC). Due to commercial confidentiality, data cannot be shared directly among information companies, which in turn prevents them from integrating resources to train better models. We need a framework that encrypts neural network models trained on the data and provides certain model exchange rewards that can be used to incentivize data sharing and to protect intellectual property (IP) and privacy of intellectual capital. Currently, federated learning provides a framework to train neural networks without compromising privacy, while block chain–based trading systems can attract other participants through a reward mechanism set by smart contracts. In this paper, we propose a block chain–based federated learning algorithm that enables reliable data sharing while protecting data from leakage, and design smart contracts based on the incentive mechanism of Shapley Values to reward data providers. We design a platform for managing IC by combining federated learning and block chain called Federated Learning Intellectual Capital Platform (FedLICP).