系统梳理联邦学习量化方法，揭示其如何提升通信效率与模型鲁棒性。
Quantization in Federated Learning: Methods, Challenges and Future Directions
- 按设备异构、非独立同分布等特性构建全新分类体系
- 量化能降低通信负载并缓解客户端漂移与收敛不稳问题
- 为移动端和边缘设备部署提供可操作的设计指南
联邦学习（FL）已成为保护隐私的分布式智能基础范式，但其可扩展性仍受制于通信瓶颈、设备异构性以及非独立同分布数据下的训练挑战。量化是缓解这些限制最有效的机制之一，可同时减少上行/下行传输负载与本地计算开销。本文首次针对联邦学习场景开展系统性综述，提出一种基于联邦学习特性的新分类体系，涵盖客户端异构性、聚合一致性、通信调度适应性、非独立同分布鲁棒性、隐私安全集成及软硬件能效协同优化等维度。除归纳现有方法外，还分析了量化与客户端漂移、部分参与、收敛稳定性、安全聚合及差分隐私等核心行为的交互关系。进一步提炼跨方法洞察，识别开放研究缺口，并为在移动、物联网与边缘平台部署量化联邦学习提供设计建议。本综述表明，量化不仅是压缩技术，更是决定现代联邦学习性能、鲁棒性与实用性的关键系统组件。
原文摘要 · Abstract (English)
Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data. Quantization is one of the most effective mechanisms for mitigating these limitations, reducing both uplink/downlink payloads and on-device computation. This paper provides the first FL-centric systematic review of quantization, introducing a novel taxonomy organized around FL-specific dimensions, including client heterogeneity, aggregation consistency, communication-scheduling adaptation, non-IID robustness, privacy/security integration, and hardware/energy co-optimization. Beyond cataloging existing methods, we analyze how quantization interacts with core FL behaviors such as client drift, partial participation, convergence stability, secure aggregation, and differential privacy. We further identify cross-method insights, open research gaps, and design guidelines for practitioners deploying quantized FL on mobile, IoT, and edge platforms. This survey thus establishes quantization not merely as a compression technique, but as a fundamental systems component shaping the performance, robustness, and practicality of modern FL.
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