论文标题
QUIC-FL：用于联合学习的快速无偏压缩
QUIC-FL: Quick Unbiased Compression for Federated Learning
论文作者
论文摘要
分布式平均值估计（DME），其中$ n $ clients与估计平均水平的参数服务器传达向量，是沟通效率高效联合学习的基本构建块。在本文中，我们通过渐近地提高编码或解码（或两者兼而有之的复杂性（或两者又），我们可以改进以前的DME技术，以实现最佳$ O（1/N）$归一化平方误差（NMSE）保证。为了实现这一目标，我们以一种新颖的方式将问题形式化，使我们可以使用现成的数学求解器来设计量化。
Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning. In this paper, we improve on previous DME techniques that achieve the optimal $O(1/n)$ Normalized Mean Squared Error (NMSE) guarantee by asymptotically improving the complexity for either encoding or decoding (or both). To achieve this, we formalize the problem in a novel way that allows us to use off-the-shelf mathematical solvers to design the quantization.
