Distributed computing is known as an emerging and efficient technique to support various intelligent services, such as large-scale machine learning. However, privacy leakage and random delays from straggling servers pose significant challenges. To address these issues, coded computing, a promising solution that combines coding theory with distributed computing, recovers computation tasks with results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, which can adaptively provide accurate or approximated results according to the form of computation functions, so as to suit diverse types of computation tasks. We prove that APCC achieves complete data privacy preservation and demonstrate its optimality in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding. To further alleviate the straggling effect and reduce delay, we integrate hierarchical task partitioning and task cancellation into the coding design of APCC. The corresponding partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. Simulation results show that APCC can reduce task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.

翻译：分布式计算被认为是支持大规模机器学习等各类智能服务的一种新兴且高效的技术。然而，隐私泄露以及来自掉队服务器的随机延迟带来了重大挑战。为解决这些问题，编码计算作为一种将编码理论与分布式计算相结合的有前景的解决方案，可通过部分工作节点的结果恢复计算任务。本文提出自适应隐私保护编码计算（APCC）策略，该策略能根据计算函数的形式自适应地提供精确或近似结果，从而适配不同类型的计算任务。我们证明APCC可实现完整的数据隐私保护，并证明其在编码率（即编码前后任务计算负载之比）方面具有最优性。为进一步缓解掉队效应并降低延迟，我们将分层任务划分与任务取消机制融入APCC的编码设计。相应的划分问题被建模为以最小化任务完成延迟为目标的混合整数非线性规划（MINLP）问题。我们提出一种低复杂度的最大值下降（MVD）算法来最优求解这些问题。仿真结果表明，相较于其他先进基准方案，APCC可将任务完成延迟降低20.3%至47.5%。