How can we optimally trade extra computing power to reduce the communication load in
distributed computing? We answer this question by characterizing a fundamental tradeoff …

Many sensor applications are interested in computing a function over measurements (eg,
sum, average, max) as opposed to collecting all sensor data. Today, such data aggregation …

We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

Compression schemes have been extensively used in Federated Learning (FL) to reduce
the communication cost of distributed learning. While most approaches rely on a bounded …

The following network computing problem is considered. Source nodes in a directed acyclic
network generate independent messages and a single receiver node computes a target …

A directed acyclic network is considered where all the terminals need to recover the sum of
the symbols generated at all the sources. We call such a network a sum-network. It is shown …

The Σ-QMAC problem is introduced, involving S servers, K classical (F d) data streams, and
T independent quantum systems. Data stream W k, k∈[K] is replicated at a subset of servers …

In this paper, we consider different aspects of the problem of compressing for function
computation across a network, which we call network functional compression. In network …

In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices
generating and uploading a deluge of data to the cloud. If successfully extracted, the …

We consider the scenario in which a set of sources generates messages in a network and a
receiver node demands an arbitrary linear function of these messages. We formulate an …