CPFL: An Effective Secure Cognitive Personalized Federated Learning Mechanism for Industry 4.0

Abstract

While promoting the intelligence in industrial production, Industry 4.0 has also caused privacy leaks concurrently. As a possible solution, the existing personalized federated learning relies too much on a good global model to fine-tune or limit local drift, which lacks intelligent cognitive ability. When faced with heterogeneous data or poisoning attacks, even a few low-quality local models will affect the whole federation effect. In this article, we design a cognitive personalized federated learning (CPFL) mechanism for Industry 4.0, which can selectively improve the collaboration capabilities between more relevant devices. We use the parameters in the local training process as the cognitive basis and calculate Earth mover’s distance to quantify the differences between different models. When the gradient distribution is closer, the local data are more similar. By adaptively adjusting the weight distribution during the aggregation process, self-learning and cooperative learning are balanced, and the interference of heterogeneous data on the federated training process is reduced. Therefore, the global model can better fit most heterogeneous industrial data and achieve personalization. Comparative experimental results show that our proposed CPFL mechanism can increase the average accuracy of personalized models by 5%–10% in non independent and identically distributed situations, and it has certain effects against poisoning attacks and noise interference.