Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

A survey on MAC protocols for complex self-organizing cognitive radio networks

Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresNowadays with the current growth of information exchange, and the increasing mobility of devices, it becomes essential to use technology to monitor this development. For that P2P networks are used, the exchange of information between agencies is facilitated, these now being applied in mobile networks, including MANETs, where they have special features such as the fact that they are semi-centralized, where it takes peers more ability to make a greater role in the network. But those peer with more capacity, which are used in the optimization of various parameters of these systems, such as optimization\to research, are difficult to identify due to the fact that the network does not have a fixed topology, be constantly changing, (we like to go online and offline, to change position, etc.) and not to allow the exchange of large messages. To this end, this thesis proposes a distributed election algorithm of us greater capacity among several possible goals, enhance research in the network. This includes distinguishing characteristics, such as election without global knowledge network, minimal exchange of messages, distributed decision made without dependence on us and the possibility of influencing the election outcome as the special needs of the network

Semantic social routing in Gnutella

The objective of this project is to improve the performance of the Gnutella peer-to-peer protocol (version 0.4) by introducing a semantic-social routing model and several categories of interest. The Gnutella protocol requires peers to broadcast messages to their neighbours when they search files. The message passing generates a lot of traffic in the network, which degrades the quality of service. We propose using social networks to optimize the speed of search and to improve the quality of service in a Gnutella based peer-to-peer environment. Each peer creates and updates a “friends list” from its past experience, for each category of interest. Once peers generate their friends lists, they use these lists to semantically route queries in the network. Search messages in a given category are mainly sent to “friends” who have been useful in the past in finding files in the same category. This helps to reduce the search time and to decrease the network traffic by minimizing the number of messages circulating in the system as compared to standard Gnutella. This project will demonstrate by simulating a peer-to-peer type of environment with the JADE multi-agent system platform that by learning other peers’ interests, building and exploiting their social networks (friends lists) to route queries semantically, peers can get more relevant resources faster and with less traffic generated, i.e. that the performance of the Gnutella system can be improved

Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.Comment: PhD thesi

Graph learning in robotics: a survey

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applications. To fully unlock their potential, hence, we propose a review of graph neural architectures from a robotics perspective. The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings, related for example to the integration of perception, decision-making, and control. Finally, the paper provides an extensive review of various robotic applications that benefit from learning on graph structures, such as bodies and contacts modelling, robotic manipulation, action recognition, fleet motion planning, and many more. This survey aims to provide readers with a thorough understanding of the capabilities and limitations of graph neural architectures in robotics, and to highlight potential avenues for future research

A novel service discovery model for decentralised online social networks.

Online social networks (OSNs) have become the most popular Internet application that attracts billions of users to share information, disseminate opinions and interact with others in the online society. The unprecedented growing popularity of OSNs naturally makes using social network services as a pervasive phenomenon in our daily life. The majority of OSNs service providers adopts a centralised architecture because of its management simplicity and content controllability. However, the centralised architecture for large-scale OSNs applications incurs costly deployment of computing infrastructures and suffers performance bottleneck. Moreover, the centralised architecture has two major shortcomings: the single point failure problem and the lack of privacy, which challenges the uninterrupted service provision and raises serious privacy concerns. This thesis proposes a decentralised approach based on peer-to-peer (P2P) networks as an alternative to the traditional centralised architecture. Firstly, a self-organised architecture with self-sustaining social network adaptation has been designed to support decentralised topology maintenance. This self-organised architecture exhibits small-world characteristics with short average path length and large average clustering coefficient to support efficient information exchange. Based on this self-organised architecture, a novel decentralised service discovery model has been developed to achieve a semantic-aware and interest-aware query routing in the P2P social network. The proposed model encompasses a service matchmaking module to capture the hidden semantic information for query-service matching and a homophily-based query processing module to characterise user’s common social status and interests for personalised query routing. Furthermore, in order to optimise the efficiency of service discovery, a swarm intelligence inspired algorithm has been designed to reduce the query routing overhead. This algorithm employs an adaptive forwarding strategy that can adapt to various social network structures and achieves promising search performance with low redundant query overhead in dynamic environments. Finally, a configurable software simulator is implemented to simulate complex networks and to evaluate the proposed service discovery model. Extensive experiments have been conducted through simulations, and the obtained results have demonstrated the efficiency and effectiveness of the proposed model.University of Derb