关键词: 联邦学习, 区块链, 差分隐私, 激励机制, 异常检测

Abstract:

A random verification blockchain construction and privacy protection method for federated learning was proposed to address the issues such as local device model gradient leakage， the ability of centralized server devices to exit at will， and the inability of global models to resist malicious user attacks in existing federated learning models. Firstly， blockchain leadership nodes were elected randomly by introducing verifiable hash functions， thereby ensuring the fairness of voting a node to create block. Secondly， a verification node cross detection mechanism was designed to defend against malicious node attacks. Finally， based on differential privacy technology， blockchain nodes were trained， and incentive rules were constructed on the basis of the contribution of nodes to the model to enhance the training accuracy of the federated learning model. Experimental results show that the proposed method achieves 80% accuracy for malicious node poisoning attacks with 20% malicious nodes， which is 61 percentage points higher than that of Google FL， and the gradient matching loss of the proposed method is 14 percentage points higher than that of Google FL when the noise variance is 10^-3. It can be seen that compared to the federated learning methods such as Google FL， the proposed method can ensure good accuracy while improving the security of the model， and has better security and robustness.

Key words: federated learning, blockchain, differential privacy, incentive mechanism, anomaly detection