The randomized distributed function computation (RDFC) framework, which unifies many cutting-edge distributed computation and learning applications, is considered. An autoencoder (AE) architecture is proposed to minimize the total variation distance between the probability distribution simulated by the AE outputs and an unknown target distribution, using only data samples. We illustrate significantly high RDFC performance with communication load gains from our AEs compared to data compression methods. Our designs establish deep learning-based RDFC methods and aim to facilitate the use of RDFC methods, especially when the amount of common randomness is limited and strong function computation guarantees are required.

翻译：本文研究了随机分布式函数计算（RDFC）框架，该框架统一了众多前沿的分布式计算与学习应用。我们提出了一种自编码器（AE）架构，仅利用数据样本，旨在最小化AE输出所模拟的概率分布与未知目标分布之间的总变差距离。实验表明，与数据压缩方法相比，我们的自编码器在RDFC性能上实现了显著提升，并获得了通信负载增益。我们的设计建立了基于深度学习的RDFC方法，旨在促进RDFC方法的应用，特别是在公共随机性有限且需要强函数计算保证的场景中。