The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client devices and the central parameter server. In this paper, we systematically classify gradient and model compression schemes into three categories based on the type of correlations they exploit: structural, temporal, and spatial. We examine the sources of such correlations, propose quantitative metrics for measuring their magnitude, and reinterpret existing compression methods through this unified correlation-based framework. Our experimental studies demonstrate that the degrees of structural, temporal, and spatial correlations vary significantly depending on task complexity, model architecture, and algorithmic configurations. These findings suggest that algorithm designers should carefully evaluate correlation assumptions under specific deployment scenarios rather than assuming that they are always present. Motivated by these findings, we propose two adaptive compression designs that actively switch between different compression modes based on the measured correlation strength, and we evaluate their performance gains relative to conventional non-adaptive approaches. In summary, our unified taxonomy provides a clean and principled foundation for developing more effective and application-specific compression techniques for FL systems.

翻译：联邦学习（FL）中的通信瓶颈促使人们广泛研究减少客户端设备与中央参数服务器之间交换数据量的技术。在本文中，我们根据压缩方案所利用的相关性类型，将梯度和模型压缩方案系统性地分为三类：结构性、时间性和空间性。我们研究了这些相关性的来源，提出了量化其强度的度量指标，并通过这一统一的相关性框架重新解读了现有的压缩方法。我们的实验研究表明，结构性、时间性和空间相关性的程度因任务复杂度、模型架构和算法配置的不同而显著变化。这些发现表明，算法设计者应根据具体部署场景仔细评估相关性假设，而非假设它们始终存在。基于这些发现，我们提出了两种自适应压缩设计，根据测量的相关性强度主动切换不同的压缩模式，并评估了它们相对于传统非自适应方法的性能提升。总之，我们的统一分类法为开发更有效且针对特定应用的联邦学习系统压缩技术提供了清晰且原则性的基础。