近似编码计算：迈向快速、隐私且安全的分布式机器学习
Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning
AI总结：
本文提出SPACDC方案，通过椭圆曲线加密保障传输安全并容忍落后节点，同时提供训练数据的信息论隐私保护，实验证明其深度学习算法相比基线实现显著加速。
摘要：
在大规模分布式机器学习系统中，编码计算因其能够有效缓解落后节点（stragglers）的影响而受到广泛关注。然而，若干新出现的问题极大地限制了编码分布式系统的性能。首先，存在合谋工作节点（colluding workers），它们相互合谋结果，导致严重的隐私泄露问题。其次，现有工作很少考虑分布式计算系统数据传输中的安全问题。第三，需要等待的所需结果数量随着解码函数的次数增加而增加。在本文中，我们设计了一种安全且隐私的近似编码分布式计算（SPACDC）方案，可同时处理上述问题。我们的SPACDC方案使用一种基于椭圆曲线密码学的新型加密算法，保证传输过程中的数据安全。特别是，SPACDC方案不对需要等待的最小结果数量施加严格约束。我们进行了广泛的性能分析，以证明SPACDC方案的有效性。此外，我们提出了一种基于SPACDC方案的安全且隐私的分布式学习算法，该算法可以为训练数据提供信息论级别的隐私保护。我们的实验表明，基于SPACDC的深度学习算法相比基线方法实现了显著的加速。
英文摘要：
In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches.
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