The paper introduces a novel hierarchical federated learning algorithm that incorporates quantization for efficient communication and resilience to statistical heterogeneity. By combining gradient aggregation within sets and model aggregation between sets, the algorithm outperforms traditional approaches in scenarios with heterogeneous data distributions. The study provides insights into the convergence rate, system optimization, and experimental results showcasing the algorithm's effectiveness.
The research focuses on developing a hierarchical federated learning algorithm that integrates quantization to enhance communication efficiency and address statistical heterogeneity challenges. By combining gradient aggregation within sets and model aggregation between sets, the proposed algorithm demonstrates superior performance compared to conventional methods. The study offers valuable insights into convergence rates, system optimization, and experimental results validating the effectiveness of the new approach.
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by Seyed Mohamm... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01540.pdfDeeper Inquiries