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FedLSF: Federated Local Graph Learning via Specformers

FedLSF: Federated Local Graph Learning via Specformers

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international conference on distributed computing in sensor systems (dcoss)

作者： Ram Samarth BB Annappa B Sachin D N Dept of CSE Indian Institute of Information Technology Kottayam Kerala India Dept of CSE National Institute of Technology Surathkal Karnataka India

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The abundance of graphical data and associated privacy concerns in real-world scenarios highlight the need for a secure and distributed methodology utilizing Federated Learning for Graph Neural Networks(GNNs). While spatial GNNs have been explored in FL, spectral GNNs, which capture rich spectral information, remain relatively unexplored. Despite enhancing GNNs’ expressiveness through attention-based mechanisms, challenges persist in the spatial approach for FL due to cross-client edges. This work introduces two information capture methods for spectral GNNs in FL settings, Global Information Capture and Local Information Capture, which address cross-client edges. Federated Local Specformer (FedLSF) is proposed as a novel methodology that combines local information capture with state-of-the-art(SOTA) Specformer, enabling local graph learning on clients. FedLSF leverages Specformers involving spectral and attention approaches by integrating Eigen Encoding, Transformer architecture, and graph convolution. This enables capturing rich information from eigen spectra and addresses concerns related to cross-client edges through fully connected eigen-spaces. Experimental results demonstrate FedLSF’s efficacy in both homophily and heterophily datasets, showing significant accuracy improvements (2-50)% in highly non-independent and identically distributed (Non-IID) scenarios compared to the present SOTA. This research advances attention-based spectral mechanisms in FL for GNNs, providing a promising solution for preserving privacy in non-IID graph data environments. Implementation can be found at https://***/achiverram28/FedLSF-dcoss

关键词： Data privacy Accuracy Federated learning Convolution distributed databases Computer architecture Transformers

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A distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems 48

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

引用

48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hovine, Charles Bertrand, Alexander Leuven Belgium Signal Processing and Data Analytics Stadius Center for Dynamical Systems Leuven Belgium Leuven Belgium

ISBN: (纸本)9781728163277

computing the optimal solution to a spatial filtering problems in a Wireless sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting. © 2023 ieee.

关键词： Adaptive algorithms

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(POSTER) OVERTAKE: Opportunistic Routing and Concurrent Transmissions for TSCH 16

(POSTER) OVERTAKE: Opportunistic Routing and Concurrent Tran...

引用

16th Annual international conference on distributed computing in sensor systems (dcoss)

作者： Harms, Oliver Landsiedel, Olaf Univ Kiel Kiel Germany Chalmers Univ Technol Gothenburg Sweden

ISBN: (数字)9781728143514

ISBN: (纸本)9781728143514

In this paper, we present OVERTAKE, an opportunistic routing protocol for Time-Slotted Channel Hopping (TSCH). OVERTAKE combines (1) opportunistic routing, (2) concurrent transmissions and (3) TSCH. We show that this novel combination enables low-latency, central scheduling withstanding node failures. Our initial results show its ability to withstand node failures of up to 40% of nodes of a flow while keeping minimal latency.

关键词： TSCH Opportunistic Routing Concurrent Transmissions Central Scheduling Wireless sensor-Actuator Networks (Industrial) Internet of Things

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 ieee.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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An Adaptive Drone-based Multigenerational sensor System for Monitoring Large Ecosystems

An Adaptive Drone-based Multigenerational Sensor System for ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Ravi Mukkamala Stephan Olariu Meshari Aljohani Mohan Sunkara Hind Aldabagh Department of Computer Science Old Dominion University Norfolk Virginia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

With the increase in the use of sensors and drones to monitor soil and crop conditions in vast agricultural lands as well as for situational-monitoring of natural disasters such as forest fires and increasing sea levels, there is a need for systems that can support long-term monitoring with minimal cost. In this paper, we propose a novel architecture that combines sensor, drone, blockchain, and machine learning technologies to support a long-term monitoring system for vast areas of land. Long-term monitoring is achieved by employing multigenerational sensors that have been deployed at the start but are woken up in stages as and when needed to replace or reinforce existing sensors. A swarm of drones periodically scans the landscape to identify areas that need immediate attention (e.g., dry soil, pests or insects, humidity, temperature, etc.). Drones store and process the data using onboard units and make real-time decisions regarding changing path plans or changing scanning frequencies. Optionally, data is sent to the base station that fuses the information from multiple drones in the swarm, and inputs the fused data to a machine-learning model that recommends the needed actions. The actions may include obtaining additional data, awakening more sensors, actions to handle any situation by signaling for external actions such as cloud seeding, controlled chemical spraying from a drone, etc., in agricultural applications. Using simulated scenarios, we have employed three prediction algorithms—the Prophet model, CNN, and LSTM—to determine the effectiveness of these algorithms in predicting sensors values in the simulated scenarios. The proposed architecture is scalable, supports long-term monitoring, and is effective in predicting future conditions to enable preemptive actions.

关键词： Temperature sensors Chemical sensors Machine learning Computer architecture Soil Prediction algorithms sensor systems

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Efficiently Managing Large-Scale Keys in HDFS 23

Efficiently Managing Large-Scale Keys in HDFS

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作者： Jin, Wei Geng, Kui Yu, Mingjie Guo, Yunchuan Li, Fenghua Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Security University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665494571

To transparently encrypt/decrypt data stored in Hadoop distributed File System (HDFS), the Key Management Server (KMS) has been designed to automatically assign encryption zone keys (EZK) to encryption zones. However, the current key-management scheme suffers from performance bottleneck. In this paper, dividing key files for storing key data into metadata file and material file, we propose a memory-disk coordinated key management architecture for Hadoop EZK to manage massive keys, where a tree structure is designed to index key metadata stored in memory, and the inverted index structure is used to record materials stored on hard disk. Further balance-tree-based management algorithms without Java serialization is designed to efficiently query keys (including metadata and materials). A series of theoretical analysis are conducted to show the upper and lower boundary of metadata query. Finally, we implement the above scheme in the Hadoop KMS without modifying the original key-management interfaces. A series of experiments are carried out on the simulated data. Experimental results show that, our scheme can efficiently manage ten billion keys and perform much better than the existing KMS in Hadoop. © 2021 ieee.

关键词： Java Smart cities File systems Memory management distributed databases Metadata Hard disks

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distributed data reduction with decision-making for IoT-based smart farming irrigation systems 14

Distributed data reduction with decision-making for IoT-base...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Hasan, Zahraa Yaseen Kadhum Idrees, Ali University of Babylon Department of Software Babylon Iraq University of Babylon Department of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

The more crucial process and one of the world's major consumers of water in agriculture is irrigation, which has been growing as a result of population growth and the resulting rise in demand for food. The irrigation system is one of the most important components of a system of agriculture. The crop's productivity will be directly influenced by the efficiency and applicability of the irrigation system. Water scarcity is one of the greatest challenges for an irrigation system. The efficient use of land, water, and energy resources depends on Internet of Things (IoT)-based smart agriculture systems. In this article, a distributed data reduction and decision-making (DiDaReD) approach for IoT-based smart farming irrigation systems is proposed. The DiDaReD approach is implemented on two levels: sensor devices and edge gateway. At the sensor device level, we implemented a lightweight scoring method for removing the redundant collected soil moisture readings before sending them to the edge gateway. At the edge of the gateway, a voting technique is applied to the scores of received readings from sensor devices to produce a decision regarding irrigation of the monitored farming field according to the soil moisture state of this field. Finally, since the DiDaReD approach is periodic and works in real-time, we implement a decision reduction algorithm to prevent sending the same decision notifications to the actuator, thus saving the energy of the IoT network. Several experiments are implemented using real sensed data from the farming field, and the conducted results show that the proposed DiDaReD outperforms other methods in terms of data reduction ratio, number of sent readings, energy consumption, network lifetime, data integrity, decisions, and total energy consumed by the edge-gateway node. © 2023 ieee.

关键词： Internet of things

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Proceedings - 2023 ieee international conference on Cloud Engineering, IC2E 2023

Proceedings - 2023 IEEE International Conference on Cloud En...

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11th ieee international conference on Cloud Engineering, IC2E 2023

ISBN: (纸本)9798350343946

The proceedings contain 31 papers. The topics discussed include: cost-effective scheduling for Kubernetes in the edge-to-cloud continuum;energy-efficient OECT sensor data analysis on constrained edge devices;CLOUDFACTORY: an open toolkit to generate production-like workloads for cloud infrastructures;a reinforcement learning approach for performance-aware reduction in power consumption of data center compute nodes;protocol-independent context propagation for sharing microservices in multiple environments;towards serverless sky computing: an investigation on global workload distribution to mitigate carbon intensity, network latency, and cost;evaluation of data enrichment methods for distributed stream processing systems;and breaking the vicious circle: self-adaptive microservice circuit breaking and retry.

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Advancements in Federated Learning for Health Applications: A Concise Survey

Advancements in Federated Learning for Health Applications: ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Vasileios Stamatis Panagiotis Radoglou-Grammatikis Antonios Sarigiannidis Nikolaos Pitropakis Thomas Lagkas Vasileios Argyriou Evangelos Markakis Panagiotis Sarigiannidis K3Y Ltd Sofia Bulgaria Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Computing Engineering & the Build Environment Edinburgh Napier University Edinburgh UK Department of Computer Science International Hellenic University Kavala Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Smart solutions in the healthcare domain have garnered considerable attention due to their potential to enhance standard treatment methods and improve overall health. However, privacy concerns often prevent the sharing of healthcare data, which can limit the scope for improvement. In this context, Federated Learning (FL) has emerged as a transformative paradigm in machine learning. It enables collaborative model training across decentralised devices while preserving data privacy and security. This approach has gained significant traction in recent years, particularly within the healthcare sector. It offers unprecedented opportunities to harness collective intelligence from diverse healthcare datasets without compromising sensitive patient information. This survey paper summarises numerous research works that focus on the application of FL to address various healthcare challenges. Moreover, a comparison of these works is conducted, summarising the different technologies employed in each case. Therefore, in light of the previous remarks, this paper provides an up-to-date overview of the state of the art in the application of FL in the healthcare industry.

关键词： Surveys Training Data privacy Federated learning Surveillance Medical services Real-time systems

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MicaPen: A Pen to Write in Air Using Mica Motes 16

MicaPen: A Pen to Write in Air Using Mica Motes

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16th Annual international conference on distributed computing in sensor systems (dcoss)

作者： Pal, Amitangshu Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781728143514

This paper demonstrates a technique to write in air using Micaz motes. The application, named MicaPen is low-cost and can be used by the disabled who does not have fingers or limbs. Patients can wear the mote as a wrist watch or bracelet and move their hands based on what character they want to write. The mote detects the movement pattern using the accelerometer of MTS310 sensorboard and displays the characters on the screen. The application can also be extended in several applications including virtual touchscreen or remotely interact with different controlled devices.

关键词： Write in air accelerometer hand-gestures alphabets human-sensor interaction

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