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| With the rapid development of technologies such as machine learning, 5G communication, edge computing, artificial intelligence and blockchain, the field of machine learning has produced some new training methods. Among them, federated learning is a typical representative of distributed machine learning. Compared with traditional machine learning, federated learning can collaborate on model training between organizations without exchanging original data sets, which ensures the security and privacy of organizational data. As a kind of distributed machine learning, federated learning is faced with severe technical challenges in the process of model training: low participation in the training of edge nodes, untrustworthy edge nodes and untraceable training data. To solve the above problems, based on the federated learning theory and the blockchain theory, this paper carries out relevant research on the federated learning algorithm based on hybrid blockchain and the blockchain incentive mechanism. The main research achievements and innovations of this paper are as follows: the existing federated learning algorithm hides training data, which gives the attacker an opportunity to exploit, and the attacker can use this defect to carry out backdoor attacks on model training. In addition, in the training process of federated learning algorithm, the identity of the participating nodes is not authenticated, so that the attacker can pretend the nodes to contribute dirty data, which reduces the accuracy of model training. Therefore, this paper proposes a federated learning algorithm based on hybrid blockchain, which mainly adopts consortium blockchain to authenticate and manage the identity of nodes participating in training. Meanwhile, public blockchain is used to store training parameters to achieve traceability of training data. In addition, the introduction of blockchain architecture enables federated learning to be further decentralized. The results of simulation experiments show that the proposed scheme has advantages in robustness and accuracy of model training under the same training task. |
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| Keywords:Federated Learning; Blockchain; machine learning |
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