FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning

Machine learning (ML) has been drawn to attention from both academia and industry thanks to outstanding advances and its potential in many fields. Nevertheless, data collection for training models is a difficult task since there are many concerns on privacy and data breach reported recently. Data owners or holders are usually hesitant to share their private data. Also, the benefits from analyzing user data are not distributed to users. In addition, due to the lack of incentive mechanism for sharing data, ML builders cannot leverage the massive data from many sources. Thus, this paper introduces a collaborative approach for building artificial intelligence (AI) models, named FedChain to encourage many data owners to cooperate in the training phase without sharing their raw data. It helps data holders ensure privacy preservation for the collaborative training right on their premises, while reducing the computation load in case of centralized training. More specifically, we utilize federated learning (FL)and Hyperledger Sawtooth Blockchain to set up a prototype framework that enables many parties to join, contribute and receive rewards transparently from their training task results. Finally, we conduct experiments of our FedChain on cyber threat intelligence context, where AI model is trained within many organizations on each their private datastore, and then it is used for detecting malicious actions in the network. Experimental results with the CICIDS-2017 dataset prove that the FL-based strategy can help create effective privacy-preserving ML models while taking advantage of diverse data sources from the community.