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Theodoros Salonidis

Bio: Theodoros Salonidis is an academic researcher from IBM. The author has contributed to research in topics: Wireless network & Enhanced Data Rates for GSM Evolution. The author has an hindex of 32, co-authored 129 publications receiving 5226 citations. Previous affiliations of Theodoros Salonidis include University of Maryland, College Park & Rice University.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place, and propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget.
Abstract: Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

1,441 citations

Journal ArticleDOI
TL;DR: A hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users and captures the diverse objectives of the involved entities and guarantees significant benefits for each.
Abstract: The design of efficient Demand Response (DR) mechanisms for the residential sector entails significant challenges, due to the large number of home users and the negligible impact of each of them on the market. In this paper, we introduce a hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users. The operator seeks to minimize the smart grid operational cost and offers rewards to aggregators toward this goal. Profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users in order to modify their preferable consumption pattern. Finally, end-users seek to optimize the tradeoff between earnings received from the aggregator and discomfort from having to modify their pattern. Based on this market model, we first address the benchmark scenario from the point of view of a cost-minimizing operator that has full information about user demands. Then, we consider a DR market, where all entities are self-interested and non-cooperative. The proposed market scheme captures the diverse objectives of the involved entities and, compared to flat pricing, guarantees significant benefits for each. Using realistic demand traces, we quantify the arising DR benefits. Interestingly, users that are extremely willing to modify their consumption pattern do not derive maximum benefit.

471 citations

Proceedings ArticleDOI
22 Apr 2001
TL;DR: This paper introduces the Bluetooth topology construction protocol (BTCP), an asynchronous distributed protocol for constructing scatternets which starts with nodes that have no knowledge of their surroundings and terminates with the formation of a connected network satisfying all connectivity constraints posed by the Bluetooth technology.
Abstract: Wireless ad hoc networks have been a growing area of research. While there has been considerable research on the topic of routing in such networks, the topic of topology creation has not received due attention. This is because almost all ad hoc networks to date have been built on top of a single channel, broadcast based wireless media, such as 802.11 or IR LANs. For such networks the distance relationship between the nodes implicitly (and uniquely) determines the topology of the ad hoc network. Bluetooth is a promising new wireless technology, which enables portable devices to form short-range wireless ad hoc networks and is based on a frequency hopping physical layer. This fact implies that hosts are not able to communicate unless they have previously discovered each other by synchronizing their frequency hopping patterns. Thus, even if all nodes are within direct communication range of each other, only those nodes which are synchronized with the transmitter can hear the transmission. To support any-to-any communication, nodes must be synchronized so that the pairs of nodes (which can communicate with each other) together form a connected graph. Using Bluetooth as an example, this paper first provides deeper insights into the issue to link establishment in frequency hopping wireless systems. It then introduces the Bluetooth topology construction protocol (BTCP), an asynchronous distributed protocol for constructing scatternets which starts with nodes that have no knowledge of their surroundings and terminates with the formation of a connected network satisfying all connectivity constraints posed by the Bluetooth technology. To the best of our knowledge, the work presented in this paper is the first attempt at building Bluetooth scatternets using distributed logic and is quite "practical" in the sense that it can be implemented using the communication primitives offered by the Bluetooth 1.0 specifications.

392 citations

Proceedings ArticleDOI
16 Apr 2018
TL;DR: This paper analyzes the convergence rate of distributed gradient descent from a theoretical point of view, and proposes a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget.
Abstract: Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

343 citations

Proceedings ArticleDOI
23 Apr 2006
TL;DR: A new analytical model is developed that incorporates this lack of coordination, identifies dominating and starving flows and accurately predicts per-flow throughput in a large-scale network, and proposes metrics that quantify throughput imbalances due to the MAC protocol operation.
Abstract: Multi-hop wireless networks employing random access protocols have been shown to incur large discrepancies in the throughputs achieved by the flows sharing the network. Indeed, flow throughputs can span orders of magnitude from near starvation to many times greater than the mean. In this paper, we address the foundations of this disparity. We show that the fundamental cause is not merely differences in the number of contending neighbors, but a generic coordination problem of CSMA-based random access in a multi-hop environment. We develop a new analytical model that incorporates this lack of coordination, identifies dominating and starving flows and accurately predicts per-flow throughput in a large-scale network. We then propose metrics that quantify throughput imbalances due to the MAC protocol operation. Our model and metrics provide a deeper understanding of the behavior of CSMA protocols in arbitrary topologies and can aid the design of effective protocol solutions to the starvation problem.

273 citations


Cited by
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Journal ArticleDOI
TL;DR: This work introduces a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federatedLearning, and federated transfer learning, and provides a comprehensive survey of existing works on this subject.
Abstract: Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

2,593 citations

Journal ArticleDOI
01 Jul 2003
TL;DR: The important role that mobile ad hoc networks play in the evolution of future wireless technologies is explained and the latest research activities in these areas are reviewed, including a summary of MANETs characteristics, capabilities, applications, and design constraints.
Abstract: Mobile ad hoc networks (MANETs) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary, ‘‘ad-hoc’’ network topologies, allowing people and devices to seamlessly internetwork in areas with no pre-existing communication infrastructure, e.g., disaster recovery environments. Ad hoc networking concept is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad hoc paradigm. Recently, the introduction of new technologies such as the Bluetooth, IEEE 802.11 and Hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent evolutions have been generating a renewed and growing interest in the research and development of MANET. This paper attempts to provide a comprehensive overview of this dynamic field. It first explains the important role that mobile ad hoc networks play in the evolution of future wireless technologies. Then, it reviews the latest research activities in these areas, including a summary of MANETs characteristics, capabilities, applications, and design constraints. The paper concludes by presenting a set of challenges and problems requiring further research in the future. � 2003 Elsevier B.V. All rights reserved.

1,430 citations

Posted Content
TL;DR: This work proposes building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.
Abstract: Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

1,317 citations

Journal ArticleDOI
12 Jun 2019
TL;DR: A comprehensive survey of the recent research efforts on edge intelligence can be found in this paper, where the authors review the background and motivation for AI running at the network edge and provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the edge.
Abstract: With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

977 citations