Reviews: Straggler Mitigation in Distributed Optimization Through Data Encoding

This paper addresses the issue of performing distributed optimization in the presence of straggling/slow computation units. In particular, the paper focuses on the problem of linear regression min_w \ Xw - y\ _2, ---- (1) where X [(X_1) T, (X_2) T,..., (X_m) T] T and y [y_1, y_2,..., y_m] T denote the data points and the corresponding labels. In general, the distributed setup with m worker nodes allocates i -th data point X_i and the associated label y_i to i -th worker node. The linear regression problem is then solved in an iterative manner where messages/information needs to be communicated among (master) server and the worker nodes. However, in practice, some of the workers (aka stragglers) take longer time to completer their end of processing/communication, which slows down the entire distributed optimization problem.