This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.

翻译：本文提出了一种新颖的三层联邦学习架构，用于优化边缘计算环境。该架构解决了客户端数据异构性和计算约束相关的挑战，引入了一个可扩展且保护隐私的框架，提升了分布式机器学习的效率。通过实验，本文证明了该架构比传统联邦学习模型更有效地管理非独立同分布数据集的能力。此外，文章强调了这种创新方法在显著提高模型准确性、降低通信开销以及促进联邦学习技术更广泛采用方面的潜力。