Distributed Learning based on 1-Bit Gradient Coding in the Presence of
  Stragglers
Chengxi Li Mikael Skoglund
This paper considers the problem of distributed learning (DL) in the presence
of stragglers. For this problem, DL methods based on gradient coding have been
widely investigated, which redundantly distribute the training data to the
workers to guarantee convergence when some workers are stragglers. However,
these methods require the workers to transmit real-valued vectors during the
process of learning, which induces very high communication burden. To overcome
this drawback, we propose a novel DL method based on 1-bit gradient coding
(1-bit GCDL), where 1-bit data encoded from the locally computed gradients are
transmitted by the workers to reduce the communication overhead. We
theoretically provide the convergence guarantees of the proposed method for
both the convex loss functions and nonconvex loss functions. It is shown
empirically that 1-bit GC-DL outperforms the baseline methods, which attains
better learning performance under the same communication overhead.