Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning

Transfer learning has been developed to improve the performances of different but related tasks in machine learning. However, such processes become less efficient with the increase of the size of training data and the number of tasks. Moreover, privacy can be violated as some tasks may contain sensitive and private data, which are communicated between nodes and tasks. We propose a consensus-based distributed transfer learning framework, where several tasks aim to find the best linear support vector machine (SVM) classifiers in a distributed network. With alternating direction method of multipliers, tasks can achieve better classification accuracies more efficiently and privately, as each node and each task train with their own data, and only decision variables are transferred between different tasks and nodes. Numerical experiments on MNIST datasets show that the knowledge transferred from the source tasks can be used to decrease the risks of the target tasks that lack training data or have unbalanced training labels. We show that the risks of the target tasks in the nodes without the data of the source tasks can also be reduced using the information transferred from the nodes who contain the data of the source tasks. We also show that the target tasks can enter and leave in real-time without rerunning the whole algorithm

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Distributed routing in networks and its application

Tato diplomová práce popisuje decentralizovaný systém jménem NodeSkipper určený pro kteroukoli spojitou neorientovanou síť. Uzly v této síti mohou posílat nebo vyhledávat jiné uzly nebo vyvolat proces "consensus", kdy se celá síť shodne na hodnotě zvolené veličiny tak, aby byl výsledek ovlivněn každým uzlem a byl pro všechny uzly stejný. NodeSkipper je inspirovaný datovou strukturou Skip List, která díky náhodnosti své struktury, která se přes postupné přidávání a ubírání uzlů přibližuje zvolenému pravděpodobnostnímu rozdělení, nabízí velmi všestranný výkon a vysokou robustnost. Protokol NodeSkipper pracuje nejlépe pro sítě s efektem malého světa, který se vyskytuje ve skutečných sítích přirozeně. Díky tomuto efektu roste průměr sítě pouze logaritmicky vzhledem k množství uzlů. V takové síti je NodeSkipper schopný doručit zprávu nebo hledat uzel v logaritmickém čase. Díky své necentralizovanosti a absenci konkrétní struktury funguje velmi dobře s velkými sítěmi, kde jsou nové uzly nepředvídatelně přidávány a odebírány a přímá spojení navazována a ztrácena, jako například vozidla v silniční dopravě, doručovací roboti, stroje v továrně, bezpečnostní systémy pro velká území, počítače spolupracující na výpočetně náročné úloze nebo roboti účastnící se boje. Protože tento systém nemá žádné uzly s vyšší důležitostí, je odolný vůči cíleným útokům a vzhledem k tomu, že funguje na kterémkoli spojitém grafu, je odolný vůči náhodným útokům a selháním. Díky schopnosti dojít ke shodě může dobře koordinovat své prostředkyThis thesis describes a decentralized system that can work over any connected undirected network called NodeSkipper. Each node in this system can send a message to another node, look-up any node or request the system to reach consensus, which means that every node in the system will agree on a quantity of interest in a manner where each node influences the result. The system is designed after the Skip List data structure, which uses randomized structure that over successive entries and removals converges towards its probability distribution, while providing great all-rounded performance and robustness. The NodeSkipper protocol works best over networks with small-world effect, which occurs naturally on real networks. This effect manifests itself by network diameter scales logarithmically with the number of nodes. On such network, NodeSkipper can deliver messages and look-up nodes in logarithmical time as well. Thanks to its decentralized nature and no rigid structure, it works well with large networks where new nodes are unpredictably added and removed and direct connections gained and lost, such as cars on the road, delivery robots, machines in a manufacturing plant, large scale security system, computers working together on computationally demanding task or battle units in armed conflict. Because this system does not have any nodes of special importance, it is resistant to targeted attacks. Because it works as long as the graph is connected, it is resistant to random attacks and failures. Thanks to its ability to reach network wide consensus, it can coordinate its efforts