Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.

边缘计算正在成为一种新的范式，以允许在网络边缘附近处理数据，在网络边缘通常会生成和收集数据。这可以在诸如物联网（IoT）之类的应用程序的边缘进行关键计算，其中越来越多的设备（传感器，摄像机，健康监控设备等）收集需要通过严格的计算密集型算法处理的数据。可靠性，安全性和延迟限制。我们的关键工具是编码计算理论，该理论主张通过使用擦除码并将这些任务卸载到其他设备进行计算，从而在计算密集型任务中混合数据。编码计算由于其较高的可靠性，较小的延迟和较低的通信成本，最近引起了人们的关注。在本文中，我们通过考虑（1）IoT应用程序和设备的隐私要求以及（2）边缘设备的异构和时变资源，开发了一种用于分布式矩阵矢量乘法的私有无速率自适应编码计算（PRAC）算法。我们表明，当资源异构时，PRAC的性能优于已知的安全编码计算方法。我们为PRAC的性能及其与基准的比较提供了理论上的保证。此外，我们通过在基于Android的智能手机上的仿真和实施来确认我们的理论结果。我们表明，当资源异构时，PRAC的性能优于已知的安全编码计算方法。我们为PRAC的性能及其与基准的比较提供了理论上的保证。此外，我们通过在基于Android的智能手机上的仿真和实施来确认我们的理论结果。我们显示，当资源异构时，PRAC的性能优于已知的安全编码计算方法。我们为PRAC的性能及其与基准的比较提供了理论上的保证。此外，我们通过在基于Android的智能手机上的仿真和实施来确认我们的理论结果。