# Federated Learning for Surface Roughness
## 1 Introduction  
Federated learning, as an emerging decentralized machine learning technique, has gained significant attention in recent years, particularly in the industrial sector. It demonstrates notable advantages in data privacy protection and decentralized data management. [PERSON] et al. [1] systematically reviewed the applications of federated learning in the industrial, healthcare, and Internet of Things (IoT) domains, highlighting its outstanding performance in handling sensitive data while maintaining privacy. [PERSON] et al. [2] proposed a federated learning solution based on an edge computing framework for the Industrial IoT (IIoT), effectively enhancing production efficiency and data security. [PERSON] et al. [3] further applied federated learning to industrial artificial intelligence by integrating threshold secret sharing and homomorphic encryption to strengthen data security. However, their approach requires more communication rounds, which may reduce training efficiency. Additionally, [PERSON] et al. [4] introduced the Federated Shapley Value method to evaluate the contribution of data in federated learning. This method supports fair distribution and can effectively improve overall training efficiency while identifying low-quality participants. [PERSON] et al. [5] combined federated learning with transfer learning through the FedHealth framework to address cross-domain data integration issues in wearable devices. By encrypting model parameter transmissions and applying transfer learning to user models, they successfully improved model accuracy by 5.3%, demonstrating a strong balance betweenprivacy protection and performance. However, federated learning still faces numerous challenges in real-world applications, particularly the issue of non-independent and identically distributed (non-IID) data. [PERSON] et al. [6] proposed a data augmentation method based on Stable Diffusion, named Gen-FedSD, which effectively alleviates the problem of non-IID data distribution in federated learning. This approach significantly enhanced the model's generalization ability and achieved outstanding performance on the CIFAR-10 and CIFAR-100 datasets. [PERSON] et al. [7] conducted an in-depth discussion on federated learning in terms of privacy protection, communication efficiency, and data heterogeneity, offering suggestions and future research directions. [PERSON] et al. [8] also provided a comprehensive review of federated learning from multiple perspectives, including efficiency improvement, privacy preservation, fairness, and systemic challenges. They thoroughly examined critical unresolved issues such as non-IID data handling, communication constraints, and attack defenses. In addition, [PERSON] et al. [9] proposed a three-tier collaborative federated learning architecture, which utilizes multi-agent reinforcement learning to optimize device selection and resource allocation, effectively reducing communication costs and improving learning accuracy. [PERSON] et al. [10] developed a decentralized federated learning framework that achieves an ideal balance between communication and computation costs, offering valuable insights for practical applications. In specific research on surface roughness prediction in wire electrical discharge machining (WEDM), traditional approaches have mainly focused on the application of artificial neural networks and optimization techniques. [PERSON] et al. [11] proposed the use of an Elman-type recurrent neural network (LRNN) to predict geometric errors combined with simulated annealing (SA) to optimize cutting paths, effectively compensating for profile deviations caused by wire deformation. [PERSON] et al. [12] compared four machine learning algorithms (ELM, W-ELM, SVR, and Q-SVR) in predicting surface roughness in aluminum alloy WEDM, confirming the superior performance of the W-ELM model (R\({}^{2}\) = 0.9720), which demonstrates the strong potential of artificial intelligence techniques for surface quality prediction in non-traditional machining.