2017 25th European Signal Processing Conference (EUSIPCO)

In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entries. We propose a fully distributed algorithm for solving this problem. The approach relies on minimizing a global mean-square error criterion regularized by nondifferentiable terms to promote cooperation among neighboring clusters. A general diffusion forward–backward splitting strategy is introduced. Then, it is specialized to the case of sparsity promoting regularizers. A closed-form expression for the proximal operator of a weighted sum of $\ell _1$ -norms is derived to achieve higher efficiency. We also provide conditions on the step-sizes that ensure convergence of the algorithm in the mean and mean-square error sense. Simulations are conducted to illustrate the effectiveness of the strategy.

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Diffusion generalized maximum correntropy criterion algorithm for distributed estimation over multitask network

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This paper examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Wireless Networked Multirobot Systems in Smart Factories

Cooperation among agents across the network leads to bet ter estimation accuracy. However, in many network applications the agents infer and track different models of interest in an environment where agents do not know beforehand which models are being observed by their neighbors. In this work, we propose an adaptive and distributed clustering technique that allows agents to learn and form clusters from streaming data in a robust manner. Once clusters are formed, cooperation among agents with similar objectives then enhances the performance of the inference task. The performance of the proposed clustering algorithm is discussed by commenting on the behavior of probabilities of erroneous decision. We validate the performance of the algorithm by numerical sim ulations, that show how the clustering process enhances the mean-square-error performance of the agents across the net work.

Decentralized clustering over adaptive networks

We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest The agents do not know beforehand which model is generating their own data They also do not know which agents in their neighborhood belong to the same cluster We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance Simulation results illustrate the performance of the proposed method in comparison to other useful techniques

Decentralized Clustering and Linking by Networked Agents

There arises the need in many wireless network applications to infer and track different models of interest. Some nodes in the network are informed, where they observe the different models and send information to the uninformed ones. Each uninformed node responds to one informed node and joins its group. In this work, we suggest an adaptive and distributed clustering and partitioning approach that allows the informed nodes in the network to be clustered into many groups according to the observed models; then we apply a decentralized strategy to part the uninformed nodes into groups of approximately equal size around the informed nodes.

Decentralized partitioning over adaptive networks

In this paper, we study distributed decision-making over mobile adaptive networks where nodes in the network collect data generated by two different models. The nodes need to decide which model to estimate and track. However, they do not know beforehand which model they observe. Therefore, an effective clustering technique is needed. We apply a clustering technique that reduces the clustering error. Furthermore, introduce an additional term to the motion model to ensure that the nodes move coherently without fragmentation in the network during the decision-making process. Once the network reaches agreement on the desired model, the cooperation among nodes enhances the performance of the estimation task by relaying data throughout the network.

Sahar Khawatmi

Papers

Decentralized clustering over adaptive networks

Decentralized Clustering and Linking by Networked Agents

Decentralized partitioning over adaptive networks

Distributed decision-making over mobile adaptive networks

Decentralized Decision-Making Over Multi-Task Networks