5 research outputs found
Distributed and Private Coded Matrix Computation with Flexible Communication Load
Tensor operations, such as matrix multiplication, are central to large-scale
machine learning applications. For user-driven tasks these operations can be
carried out on a distributed computing platform with a master server at the
user side and multiple workers in the cloud operating in parallel. For
distributed platforms, it has been recently shown that coding over the input
data matrices can reduce the computational delay, yielding a trade-off between
recovery threshold and communication load. In this paper we impose an
additional security constraint on the data matrices and assume that workers can
collude to eavesdrop on the content of these data matrices. Specifically, we
introduce a novel class of secure codes, referred to as secure generalized
PolyDot codes, that generalizes previously published non-secure versions of
these codes for matrix multiplication. These codes extend the state-of-the-art
by allowing a flexible trade-off between recovery threshold and communication
load for a fixed maximum number of colluding workers.Comment: 8 pages, 6 figures, submitted to 2019 IEEE International Symposium on
Information Theory (ISIT
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
Distributed computing platforms typically assume the availability of reliable
and dedicated connections among the processors. This work considers an
alternative scenario, relevant for wireless data centers and federated
learning, in which the distributed processors, operating on generally distinct
coded data, are connected via shared wireless channels accessed via full-duplex
transmission. The study accounts for both wireless and computing impairments,
including interference, imperfect Channel State Information, and straggling
processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The
total latency of the system, obtained as the sum of computing and communication
delays, is studied for different shuffling strategies revealing the interplay
between distributed computing, coding, and cooperative or coordinated
transmission.Comment: Submitted for possible conference publicatio
Wireless Map-Reduce Distributed Computing with Full-Duplex Radios and Imperfect CSI
Consider a distributed computing system in which the worker nodes are
connected over a shared wireless channel. Nodes can store a fraction of the
data set over which computation needs to be carried out, and a
Map-Shuffle-Reduce protocol is followed in order to enable collaborative
processing. If there is exists some level of redundancy among the computations
performed at the nodes, the inter-node communication load during the Shuffle
phase can be reduced by using either coded multicasting or cooperative
transmission. It was previously shown that the latter approach is able to
reduce the high-Signal-to-Noise Ratio communication load by half in the
presence of full-duplex nodes and perfect transmit-side Channel State
Information (CSI). In this paper, a novel scheme based on superposition coding
is proposed that is demonstrated to outperform both coded multicasting and
cooperative transmission under the assumption of imperfect CSI