关键词: 数据治理, 联邦学习, 模型投毒, 恶意节点, 动态缓冲可回调

Abstract: Federated learning is a new mode of data security governance, which can make available data invisible, but federated learning is facing the threat of model poisoning attack, and its security needs to be improved. To this end, a Dynamic Cacheable Revocable (DCR) model poison defense mechanism based on federated learning is proposed. Based on the lossbased model poisoning defense method, the Dynamic threshold is calculated and used before each iteration. It makes the enemy unable to know the defense mechanism a priori, which increases the difficulty of the enemy’s attack. Moreover, the buffer period is set in the mechanism to reduce the risk of benign nodes being “killed by mistake”. At the same time, the system stores the global model parameters of each round. In case of model poisoning, the global model parameters before the round in buffer period are reloaded to achieve callback. The callable setting can reduce the negative impact of model poisoning attack on the global model, so that the federated learning model can still achieve convergence with good performance after being attacked, and ensure the security and performance of the federated learning model. Finally, in the experimental environment of TFF, the defense effect and model performance of this mechanism are verified.Key words data governance; federated learning; model poisoning; malicious node; dynamic cacheable revocable

Key words: data governance, federated learning, model poisoning, malicious node, dynamic cacheable revocable