Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

Efficient multi-objective optimization of wireless network problems on wireless testbeds

A large amount of research focuses on experimentally optimizing performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, while the number of required experiments increases exponentially for each considered design parameter. The aim of this paper is to analyze the applicability of global optimization techniques to reduce the optimization time of wireless experimentation. In particular, the paper applies the Efficient Global Optimization (EGO) algorithm implemented in the SUrrogate MOdeling (SUMO) toolbox inside a wireless testbed. The proposed techniques are implemented and evaluated in a wireless testbed using a realistic wireless conference network problem. The performance accuracy and experimentation time of an exhaustively searched experiment is compared against a SUMO optimized experiment. In our proof of concept, the proposed SUMO optimizer reaches 99.51% of the global optimum performance while requiring 10 times less experiments compared to the exhaustive search experiment

A Ring to Rule Them All - Revising OpenStack Internals to Operate Massively Distributed Clouds: The Discovery Initiative - Where Do We Are ?

STACK_HCERES2020The deployment of micro/nano data-centers in network point of presence offers an opportunity to deliver a more sustainable and efficient infrastructure for Cloud Computing. Among the different challenges we need to address to favor the adoption of such a model, the development of a system in charge of turning such a complex and diverse network of resources into a collection of abstracted computing facilities that are convenient to administrate and use is critical.In this report, we introduce the premises of such a system. The novelty of our work is that instead of developing a system from scratch, we revised the OpenStack solution in order to operate such an infrastructure in a distributed manner leveraging P2P mechanisms. More precisely, we describe how we revised the Nova service by leveraging a distributed key/value store instead of the centralized SQL backend. We present experiments that validated the correct behavior of our prototype, while having promising performance using several clusters composed of servers of the Grid’5000 testbed. We believe that such a strategy is promising and paves the way to a first large-scale and WAN-wide IaaS manager.La tendance actuelle pour supporter la demande croissante d'informatique utilitaire consiste à construire des centres de données de plus en plus grands, dans un nombre limité de lieux stratégiques. Cette approche permet sans aucun doute de satisfaire la demande actuelle tout en conservant une approche centralisée de la gestion de ces ressources, mais elle reste loin de pouvoir fournir des infrastructures répondant aux contraintes actuelles et futures en termes d'efficacité, de juridiction ou encore de durabilité. L'objectif de l'initiative DISCOVERY est de concevoir le LUC OS, un système de gestion distribuée des ressources qui permettra de tirer parti de n'importe quel noeud réseau constituant la dorsale d'Internet afin de fournir une nouvelle génération d'informatique utilitaire, plus apte à prendre en compte la dispersion géographiquedes utilisateurs et leur demande toujours croissante.Après avoir rappelé les objectifs de l'initiative DISCOVERY et expliqué pourquoi les approches type fédération ne sont pas adaptées pour opérer une infrastructure d'informatique utilitaire intégrée au réseau, nous présentons les prémisses de notre système. Nous expliquerons notamment pourquoi et comment nous avons choisi de démarrer des travaux visant à revisiter la conception de la solution Openstack. De notre point de vue, choisir d'appuyer nos travaux sur cette solution est une stratégie judicieuse à la vue de la complexité des systèmes de gestion des plateformes IaaS et de la vélocité des solutions open-source

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Une approche générique pour l'automatisation des expériences sur les réseaux informatiques

This thesis proposes a generic approach to automate network experiments for scenarios involving any networking technology on any type of network evaluation platform. The proposed approach is based on abstracting the experiment life cycle of the evaluation platforms into generic steps from which a generic experiment model and experimentation primitives are derived. A generic experimentation architecture is proposed, composed of an experiment model, a programmable experiment interface and an orchestration algorithm that can be adapted to network simulators, emulators and testbeds alike. The feasibility of the approach is demonstrated through the implementation of a framework capable of automating experiments using any combination of these platforms. Three main aspects of the framework are evaluated: its extensibility to support any type of platform, its efficiency to orchestrate experiments and its flexibility to support diverse use cases including education, platform management and experimentation with multiple platforms. The results show that the proposed approach can be used to efficiently automate experimentation on diverse platforms for a wide range of scenarios.Cette thèse propose une approche générique pour automatiser des expériences sur des réseaux quelle que soit la technologie utilisée ou le type de plate-forme d'évaluation. L'approche proposée est basée sur l'abstraction du cycle de vie de l'expérience en étapes génériques à partir desquelles un modèle d'expérience et des primitives d'expérimentation sont dérivés. Une architecture générique d'expérimentation est proposée, composée d'un modèle d'expérience générique, d'une interface pour programmer des expériences et d'un algorithme d'orchestration qui peux être adapté aux simulateurs, émulateurs et bancs d'essai de réseaux. La faisabilité de cette approche est démontrée par la mise en œuvre d'un framework capable d'automatiser des expériences sur toute combinaison de ces plateformes. Trois aspects principaux du framework sont évalués : son extensibilité pour s'adapter à tout type de plate-forme, son efficacité pour orchestrer des expériences et sa flexibilité pour permettre des cas d'utilisation divers, y compris l'enseignement, la gestion des plate-formes et l'expérimentation avec des plates-formes multiples. Les résultats montrent que l'approche proposée peut être utilisée pour automatiser efficacement l'expérimentation sur les plates-formes d'évaluation hétérogènes et pour un éventail de scénarios variés