通过标准化权重与自适应量化，显著降低通信开销并提升联邦学习性能。
FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization
- 引入权重标准化和分布感知非均匀量化，增强模型鲁棒性。
- 在极端数据异构和极低比特通信下仍保持高精度。
- 适合资源受限场景下的高效联邦学习部署。
联邦学习（FL）常因数据异构性和通信约束导致性能下降。为此，本文提出新型框架 FedWSQ，融合权重标准化（WS）与提出的分布感知非均匀量化（DANUQ）。WS 通过过滤本地更新中的偏差成分，提升模型对数据异构和客户端不稳定性抵抗能力；DANUQ 则利用本地模型更新的统计特性，最小化量化误差。实验表明，FedWSQ 在多个联邦学习基准数据集上显著降低通信开销，同时在极端数据异构和超低比特通信等挑战性设置下持续优于现有方法，保持优异模型准确率。
原文摘要 · Abstract (English)
Federated learning (FL) often suffers from performance degradation due to key challenges such as data heterogeneity and communication constraints. To address these limitations, we present a novel FL framework called FedWSQ, which integrates weight standardization (WS) and the proposed distribution-aware non-uniform quantization (DANUQ). WS enhances FL performance by filtering out biased components in local updates during training, thereby improving the robustness of the model against data heterogeneity and unstable client participation. In addition, DANUQ minimizes quantization errors by leveraging the statistical properties of local model updates. As a result, FedWSQ significantly reduces communication overhead while maintaining superior model accuracy. Extensive experiments on FL benchmark datasets demonstrate that FedWSQ consistently outperforms existing FL methods across various challenging FL settings, including extreme data heterogeneity and ultra-low-bit communication scenarios.
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