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arXiv 2310.03371cs.ITcs.DCmath.IT
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多路访问信道下分布式优化的基本极限

Fundamental Limits of Distributed Optimization over Multiple Access Channel

Shubham Jha

AI总结:

本文研究加性高斯MAC下分布式优化的收敛速率基本极限,推导了收敛下界,提出匹配该下界的数字通信方案,并分析了模拟方案在不同SNR下的性能表现。

摘要:

我们研究d维空间中的分布式优化问题,其中K个远程客户端通过加性高斯多路访问信道(Multiple Access Channel, MAC)发送编码后的梯度估计值,该信道的噪声方差为σ_z²。此外,客户端的码字必须满足平均功率约束P,由此得到的信噪比(SNR)为KP/σ_z²。本文研究了MAC对任意分布式优化算法收敛速率施加的基本极限,并设计了实现这些极限的最优通信方案。我们的第一个结果是收敛速率的下界,表明与中心化设置相比,在MAC上通信会使任何协议的收敛速度减慢√(d/(½log(1+SNR)))倍。接下来,我们设计了一种计算可行的数字通信方案,当与投影随机梯度下降算法结合时,该方案的性能与下界仅相差K的对数因子。该通信方案的核心是精心结合了多种压缩与调制思路,例如沿随机基量化、Wyner-Ziv压缩、模格解码和幅移键控。我们还表明,因易于实现而流行的模拟方案在低SNR下可获得接近最优的收敛速率,但在高SNR下会出现约√d倍的速度下降。

英文摘要:

We consider distributed optimization over a $d$-dimensional space, where $K$ remote clients send coded gradient estimates over an {\em additive Gaussian Multiple Access Channel (MAC)} with noise variance $σ_z^2$. Furthermore, the codewords from the clients must satisfy the average power constraint $P$, resulting in a signal-to-noise ratio (SNR) of $KP/σ_z^2$. In this paper, we study the fundamental limits imposed by MAC on the {convergence rate of any distributed optimization algorithm and design optimal communication schemes to achieve these limits.} Our first result is a lower bound for the convergence rate, showing that communicating over a MAC imposes a slowdown of $\sqrt{d/\frac{1}{2}\log(1+\SNR)}$ on any protocol compared to the centralized setting. Next, we design a computationally tractable {digital} communication scheme that matches the lower bound to a logarithmic factor in $K$ when combined with a projected stochastic gradient descent algorithm. At the heart of our communication scheme is carefully combining several compression and modulation ideas such as quantizing along random bases, {\em Wyner-Ziv compression}, {\em modulo-lattice decoding}, and {\em amplitude shift keying.} We also show that analog schemes, which are popular due to their ease of implementation, can give close to optimal convergence rates at low $\SNR$ but experience a slowdown of roughly $\sqrt{d}$ at high $\SNR$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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