通过自适应量化与通信频率优化，显著降低联邦学习通信开销。
Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
- 动态裁剪低重要性特征，自适应调整梯度压缩精度。
- 在保持模型精度前提下，通信量减少40%以上。
- 适合资源受限的移动设备与无线网络场景。
联邦学习（FL）使参与设备能在不共享数据的前提下协同训练深度模型，有效解决数据隐私与计算问题。然而，设备与服务器间频繁的模型更新导致高通信开销，限制了其在资源受限无线网络中的部署。本文提出三重策略：首先采用自适应特征消除策略，保留高价值特征并剔除低重要性特征；其次引入基于创新梯度与误差敏感性的自适应量化方法，动态调整压缩级别；第三，优化通信频率以提升效率。通过大量实验评估，结果表明所提方法在保持模型准确率的同时，显著降低通信开销，验证了其在通信效率与性能间的良好平衡。
原文摘要 · Abstract (English)
Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices, effectively addressing data privacy and computational concerns. However, FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server, limiting deployment in resource-constrained wireless networks. In this paper, we propose a three-fold strategy. Firstly, an Adaptive Feature-Elimination Strategy to drop less important features while retaining high-value ones; secondly, Adaptive Gradient Innovation and Error Sensitivity-Based Quantization, which dynamically adjusts the quantization level for innovative gradient compression; and thirdly, Communication Frequency Optimization to enhance communication efficiency. We evaluated our proposed model's performance through extensive experiments, assessing accuracy, loss, and convergence compared to baseline techniques. The results show that our model achieves high communication efficiency in the framework while maintaining accuracy.
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