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.

本文提出了一种新颖的三层体系结构，用于优化边缘计算环境下的联邦学习。所提出的体系结构解决了客户数据异质性和计算约束所带来的挑战。它引入了一个可扩展的、隐私保护的框架，提高了分布式机器学习的效率。通过实验，本文证明了该体系结构比传统的联邦学习模型更有效地处理非独立同分布的数据集。此外，本文还强调了这种创新方法提高模型准确性、减少通信开销和促进联邦学习技术更广泛应用的潜力。

异构恢复性联邦学习的架构蓝图