## About

64

Publications

4,305

Reads

**How we measure 'reads'**

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more

723

Citations

Citations since 2016

## Publications

Publications (64)

This paper proposes energy-efficient solutions for the smart light-emitting diode (LED) lighting system, which provides minimal energy consumption while simultaneously satisfying illuminance requirements of the users in a typical office space. In addition to artificial light from dimmable LED lamps, natural daylight coming from external sources, su...

Recent works have shown that high probability metrics with stochastic gradient descent (SGD) exhibit informativeness and in some cases advantage over the commonly adopted mean-square error-based ones. In this work we provide a formal framework for the study of general high probability bounds with SGD, based on the theory of large deviations. The fr...

We propose a communication efficient approach for federated learning in heterogeneous environments. The system heterogeneity is reflected in the presence of $K$ different data distributions, with each user sampling data from only one of $K$ distributions. The proposed approach requires only one communication round between the users and server, thus...

This paper studies probabilistic rates of convergence for consensus+innovations type of algorithms in random, generic networks. For each node, we find a lower and also a family of upper bounds on the large deviations rate function, thus enabling the computation of the exponential convergence rates for the events of interest on the iterates. Relevan...

Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task due to their limited computational resources. Thus, demanding tasks are often entirely offloaded to edge servers which can accelerate inference, however, it also causes communication cost and evokes privacy concerns. In addition, this approach leaves the computati...

We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular nonlinearity choices, like clipped, normalized, signed or quantized gradient, but we also consider novel nonlinearity choices. We establish for the considered cl...

We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range...

We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter $\lambda$. The proposed approach...

The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increase...

The number of connected Internet of Things (IoT) devices grows at an increasing rate, revealing shortcomings of current IoT networks for cyber-physical infrastructure systems to cope with ensuing device management and security issues. Data-based methods rooted in deep learning (DL) are recently considered to cope with such problems, albeit challeng...

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given “difficult” (constrained) problem via finding solutions of a sequence of “easier” (often unconstrained) subproblems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables. ALM is highly...

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier"(often unconstrained) sub-problems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables. ALM is highly...

The increasing interest in distributed energy generation from renewable sources is enabling traditional energy consumers to become active energy producers. They can be
formed into virtual clusters for easier management and to reduce costs; the virtual clusters are usually referred to as virtual microgrids (VMG). The VMGs are coordinated by
energy t...

Recently, an idling mechanism has been introduced in the context of distributed first order methods for minimization of a sum of nodes' local convex costs over a generic, connected network. With the idling mechanism, each node i, at each iteration k, is active - updates its solution estimate and exchanges messages with its network neighborhood - wi...

Smart Grid is expected to support a variety of services for energy prosumers-entities that are able both to produce and consume energy. Enabling cooperation among such prosumers in the form of energy trading may be highly beneficial for all actors in Smart Grid. However, in order to provide energy trading capabilities, a communication infrastructur...

Distributed energy trading among energy prosumers (i.e., energy producers that also consume energy) is expected to bring significant cost benefits for the participating actors. In terms of the system architecture, physical grouping into microgrids (MG) can be further enhanced by communication infrastructure that provides support for flexible organi...

The paper addresses design and analysis of communication-efficient distributed algorithms for solving weighted non-linear least squares problems in multi-agent networks. Communication efficiency is highly relevant in modern applications like cyber-physical systems and the Internet of things, where a significant portion of the involved devices have...

We examine fundamental tradeoffs in iterative distributed zeroth and first order stochastic optimization in multi-agent networks in terms of \emph{communication cost} (number of per-node transmissions) and \emph{computational cost}, measured by the number of per-node noisy function (respectively, gradient) evaluations with zeroth order (respectivel...

We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals (HSMS), which generalize classical Markov chains by introducing explicit (possibly non-geometric) distr...

The proliferation of distributed energy resources (DERs) and smart technologies has enabled the integration of microgrid generation into the energy supply chain. This paper proposes the use of energy trading agents (ETA) in the overlaying communication system in a neighbourhood area network (NAN) in which a number of microgrids (MGs) are grouped to...

We study a standard distributed optimization framework where $N$ networked nodes collaboratively minimize the sum of their local convex costs. The main body of existing work considers the described problem when the underling network is either static or deterministically varying, and the distributed optimization algorithm is of first or second order...

We establish the O($\frac{1}{k}$) convergence rate for distributed stochastic gradient methods that operate over strongly convex costs and random networks. The considered class of methods is standard each node performs a weighted average of its own and its neighbors solution estimates (consensus), and takes a negative step with respect to a noisy v...

We study detection of random signals corrupted by noise that over time switch their values (states) from a finite set of possible values, where the switchings occur at unknown points in time. We model such signals by means of a random duration model that to each possible state assigns a probability mass function which controls the statistics of dur...

Recently,
an idling mechanism has been introduced
in the context of distributed \emph{first order}
methods for minimization of
a sum of nodes' local convex costs
over a generic, connected network.
With the idling mechanism, each node~$i$,
at each iteration~$k$,
is active -- updates its
solution estimate and exchanges
messages with its network neigh...

We consider the problem of detecting a random walk on a graph, based on observations of the graph nodes. When visited by the walk, each node of the graph observes a signal of elevated mean, which we assume can be different across different nodes. Outside of the path of the walk, and also in its absence, nodes measure only noise. Assuming the Neyman...

Slotted ALOHA (SA) algorithms with Successive Interference Cancellation (SIC) decoding have received significant attention lately due to their ability to dramatically increase the throughput of traditional SA. Motivated by increased density of cellular radio access networks due to the introduction of small cells, and dramatic increase of user densi...

Slotted ALOHA (SA) algorithms with Successive Interference Cancellation (SIC) decoding have received significant attention lately due to their ability to dramatically increase the throughput of traditional SA. Motivated by increased density of cellular radio access networks due to the introduction of small cells, and dramatic increase of user densi...

We consider unconstrained minimization of a finite sum of
$N$ continuously differentiable, not necessarily convex, cost functions. Several gradient-like (and more generally, line search) methods, where the full gradient (the sum of $N$ component costs' gradients) at each iteration~$k$ is replaced with an inexpensive approximation based on
a sub-sam...

We consider distributed optimization problems where nodes in a connected network
collaboratively minimize the sum of their locally known convex costs
subject to a common (vector-valued) optimization variable. In this paper, we present a mechanism to
significantly improve the computational and communication efficiency
of some recently proposed first...

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 knowledge buried in the data can significantly improve the quality of life and foster economic growth. However, a critical bottleneck for realising the efficient IoT is the...

The 3GPP machine type communications (MTC) service is expected to contribute a dominant share of the IoT traffic via the upcoming fifth generation (5G) mobile cellular systems. MTC has ambition to connect billions of devices to communicate their data to MTC applications for further processing and data analysis. However, for majority of the applicat...

We consider distributed optimization problems where networked nodes
cooperatively minimize the sum of their locally known convex costs. A popular
class of methods to solve these problems are the distributed gradient methods,
which are attractive due to their inexpensive iterations, but have a drawback
of slow convergence rates. This motivates the i...

We review an algorithm for Cooperative Slotted ALOHA for Multi-Base Station Systems, based on cooperative decoding algorithms (spatial and spatial-temporal cooperation). This concept is promising for future usage in a multi-tier environment for 5G cellular networks as possible solution of Random Access strategy in the M2M (Machine-to-Machine) servi...

We introduce a neighborhood-based data access model for distributed coded
storage allocation. Storage nodes are connected in a generic network and data
is accessed locally: a user accesses a randomly chosen storage node, which
subsequently queries its neighborhood to recover the data object. We aim at
finding an optimal allocation that minimizes th...

We find large deviations rates for consensus-based distributed inference for
directed networks. When the topology is deterministic, we establish the large
deviations principle and find exactly the corresponding rate function, equal at
all nodes. We show that the dependence of the rate function on the stochastic
weight matrix associated with the net...

We consider distributed optimization where $N$ nodes in a connected network
minimize the sum of their local costs subject to a common constraint set. We
propose a distributed projected gradient method where each node, at each
iteration $k$, performs an update (is active) with probability $p_k$, and stays
idle (is inactive) with probability $1-p_k$....

We introduce a framework to study slotted Aloha with cooperative base
stations. Assuming a geographic-proximity communication model, we propose
several decoding algorithmswith different degrees of base stations' cooperation
(non-cooperative, spatial, temporal, and spatio-temporal). With spatial
cooperation, neighboring base stations inform each oth...

We study multiple base station, multi-access systems in which the user-base
station adjacency is induced by geographical proximity. At each slot, each user
transmits (is active) with a certain probability, independently of other users,
and is heard by all base stations within the distance $r$. Both the users and
base stations are placed uniformly a...

We consider framed slotted Aloha where $m$ base stations cooperate to decode
messages from $n$ users. Users and base stations are placed uniformly at random
over an area. At each frame, each user sends multiple replicas of its packet
according to a prescribed distribution, and it is heard by all base stations
within the communication radius $r$. Ba...

We study slotted Aloha with multiple base stations, where m base stations cooperate to decode signals from n users. Both users and base stations are placed uniformly at random over an area. Each user transmits its packet replicas at multiple slots and is heard by all base stations in its geographical vicinity. We present cooperative decoding algori...

We study the asymptotic exponential decay rate I for the convergence in probability of products WkWk-1...W1 of random symmetric, stochastic matrices Wk. Albeit it is known that the probability P that the product WkWk-1...W1 is ∈ away from its limit converges exponentially fast to zero, i.e., P ~ e-kI, the asymptotic rate I has not been computed bef...

We study the products Wk···W1 of random stochastic, not necessarily symmetric matrices. It is known that, under certain conditions, the product Wk · · · W1 converges almost surely (a.s.) to a random rank-one matrix; the latter is equivalent to |λ2(Wk · · · W1)| → 0 a.s., where λ2(·) is the second largest (in modulus) eigenvalue. In this paper, we s...

We find the large deviation rate I for convergence in probability of the product Wk ---W1W0 of temporally dependent random stochastic matrices. As the model for temporal dependencies, we adopt the Markov chain whose set of states is the set of all possible graphs that support the matrices Wk. Such model includes, for example, 1) token-based protoco...

Distributed consensus and other linear systems with system stochastic
matrices $W_k$ emerge in various settings, like opinion formation in social
networks, rendezvous of robots, and distributed inference in sensor networks.
The matrices $W_k$ are often random, due to, e.g., random packet dropouts in
wireless sensor networks. Key in analyzing the pe...

We establish the large deviations asymptotic performance (error exponent) of
consensus+innovations distributed detection over random networks with generic
(non-Gaussian) sensor observations. At each time instant, sensors 1) combine
theirs with the decision variables of their neighbors (consensus) and 2)
assimilate their new observations (innovation...

We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between the distributions of the selected measurements un...

We study, by large deviations analysis, the asymptotic performance of Gaussian running consensus distributed detection over random networks; in other words, we determine the exponential decay rate of the detection error probability. With running consensus, at each time step, each sensor averages its decision variable with the neighbors' decision va...

We study the large deviations performance of consensus+innovations distributed detection over random networks, where each sensor, at each time k, weight averages its decision variable with its neighbors decision variables (consensus), and accounts for its new observation (innovation). Sensor observations are independent identically distributed (i.i...

We show that distributed detection over random networks, or using a random protocol, e.g., of the gossip type, is asymptotically optimal, if the rate of information flow across the random network is large enough. Asymptotic optimality is in the sense of Chernoff information; in other words, we determine when the exponential rate of decay of the err...

We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random networks. At each time step $k$ each sensor: 1) averages its decision variable with the neighbors' decision variables; and 2) accounts on-the-fly for its new observation. We show that distributed dete...

We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation type algorithm, recently proposed. At each time step k, the state at each sensor is updated by a local averaging of the sensor's own state and the states of its neighbors (co...

We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation type algorithm, recently proposed. At each time step k, the state at each sensor is updated by a local averaging of the sensor's own state and the states of its neighbors (co...

Withdrawn. Comment: see under arXiv:1010.5163v1 [cs.IT]

We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection, in a wireless sensor network (WSN). Occurrence of the event of interest is modeled as a binary Gaussian hypothesis test. In this case sensor selection consists of finding, among all (<sub>p</sub> <sup>n</sup>) combinations, the one maximizing the...

This paper addresses robust linear dimensionality reduction (RLDR) for binary Gaussian hypothesis testing. The goal is to find a linear map from the high dimensional space where the data vector lives to a low dimensional space where the hypothesis test is carried out. The linear map is designed to maximize the detector performance. This translates...

## Projects

Projects (2)

The goal of this project is to develop a novel integrated decision support mechanism embedding intelligent sensing, communications and data processing methodology for improving sustainability of smart buildings through new insights, approaches and technologies for acquisition, communications, and extraction of useful information from the sheer volume of sensed data in the built environment.
http://sensible.eee.strath.ac.uk/index.html