用神经网络模拟分布式计算中的随机函数，提升通信效率。
Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation
- 用自编码器逼近目标概率分布，仅需数据样本即可训练。
- 相比传统压缩方法，通信负载显著降低，性能明显提升。
- 适合随机资源有限且需强计算保证的场景，如隐私计算。
考虑统一多种前沿分布式计算与学习应用的随机分布式函数计算（RDFC）框架。提出一种自编码器（AE）架构，仅通过数据样本最小化自编码器输出与未知目标分布之间的总变差距离。实验表明，相较于数据压缩方法，所提AE在通信负载方面取得显著增益，显著提升RDFC性能。该设计建立了基于深度学习的RDFC方法，旨在促进RDFC在共随机资源受限且需要强函数计算保障场景下的应用。
原文摘要 · Abstract (English)
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.
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