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.

中文翻译：

---

CPFL：适用于工业 4.0 的有效安全认知个性化联合学习机制

在推动工业生产智能化的同时，工业4.0也同时引发了隐私泄露。作为一种可能的解决方案，现有的个性化联邦学习过于依赖良好的全局模型来微调或限制局部漂移，缺乏智能认知能力。当面对异构数据或中毒攻击时，即使是一些低质量的本地模型也会影响整个联邦效应。在本文中，我们为工业 4.0 设计了一种认知个性化联合学习（CPFL）机制，该机制可以选择性地提高更多相关设备之间的协作能力。我们以局部训练过程中的参数为认知基础，计算推土机距离，量化不同模型之间的差异。当梯度分布更接近时，本地数据更相似。通过在聚合过程中自适应调整权重分布，平衡自学习和合作学习，减少异构数据对联合训练过程的干扰。因此，全局模型可以更好地拟合大多数异构工业数据，实现个性化。对比实验结果表明，我们提出的 CPFL 机制可以在非独立同分布情况下将个性化模型的平均准确率提高 5%~10%，并且对中毒攻击和噪声干扰具有一定的效果。减少了异构数据对联邦训练过程的干扰。因此，全局模型可以更好地拟合大多数异构工业数据，实现个性化。对比实验结果表明，我们提出的 CPFL 机制可以在非独立同分布情况下将个性化模型的平均准确率提高 5%~10%，并且对中毒攻击和噪声干扰具有一定的效果。减少了异构数据对联邦训练过程的干扰。因此，全局模型可以更好地拟合大多数异构工业数据，实现个性化。对比实验结果表明，我们提出的 CPFL 机制可以在非独立同分布情况下将个性化模型的平均准确率提高 5%~10%，并且对中毒攻击和噪声干扰具有一定的效果。