  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 (i) the privacy requirements of IoT applications and
devices, and (ii) 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.
