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ADS-CNN: Adaptive Dataflow Scheduling for lightweight CNN accelerator on FPGAs Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-20 Yi Wan, Xianzhong Xie, Junfan Chen, Kunpeng Xie, Dezhi Yi, Ye Lu, Keke Gai
Lightweight convolutional neural networks (CNNs) enable lower inference latency and data traffic, facilitating deployment on resource-constrained edge devices such as field-programmable gate arrays (FPGAs). However, CNNs inference requires access to off-chip synchronous dynamic random-access memory (SDRAM), which significantly degrades inference speed and system power efficiency. In this paper, we
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SkySwapping: Entanglement resupply by separating quantum swapping and photon exchange Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-17 Alin-Bogdan Popa, Bogdan-Călin Ciobanu, Voichiţa Iancu, Florin Pop, Pantelimon George Popescu
We propose a fast, satellite-based, on-demand entanglement resupply protocol which leverages entangled pairs exchanged in advance between satellites and ground stations. At the request time, the protocol does not require any quantum information exchange between ground and satellite, by performing the quantum entanglement swapping on satellite-level only, thus separating the particle exchange phase
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A Blockchain-based Digital Twin for IoT deployments in logistics and transportation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-17 Salvador Cuñat Negueroles, Raúl Reinosa Simón, Matilde Julián, Andreu Belsa, Ignacio Lacalle, Raúl S-Julián, Carlos E. Palau
Digital Twins are software technologies that enable the modelling of real-world phenomena in digitised environments, representing and monitoring the reality of various processes, including IoT deployments. Since 2017, the use of Digital Twins has been increasing. However, in the road transport and logistics realm, the adoption rate remains low, primarily due to the costs of processing and validating
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TMHD: Twin-Bridge Scheduling of Multi-Heterogeneous Dependent Tasks for Edge Computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Wei Liang, Jiahong Xiao, Yuxiang Chen, Chaoyi Yang, Kun Xie, Kuan-Ching Li, Beniamino Di Martino
As an efficient computing paradigm, Mobile Edge Computing (MEC) is essential in assisting mobile devices with real-time complex tasks such as big data analytics. In MEC, application tasks consist of multiple dependent subtasks, and the way to process tasks ensuring lower response latency through efficient scheduling orders is of relevant importance. Most existing research on scheduling sorts the dependent
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Sort-then-insert: A space efficient and oblivious model aggregation algorithm for top-k sparsification in federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Yongzhi Wang, Pengfei Gui, Mehdi Sookhak
Federated Learning (FL) allows multiple clients to collaboratively train machine learning models while preserving the model privacy of the clients. However, when generating a global model during the aggregation process, a malicious FL server could derive clients’ local model weights. Such a threat cannot be completely eliminated, even if model aggregation is performed in the Trusted Execution Environment
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A general framework and decentralised algorithms for collective computational processes Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Giorgio Audrito, Roberto Casadei, Gianluca Torta
Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is
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Enabling high fault-tolerant embedding capability of alternating group graphs Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Hongbin Zhuang, Xiao-Yan Li, Dajin Wang, Cheng-Kuan Lin, Kun Zhao
The Hamiltonian path/cycle serves as a robust tool for transmitting messages within parallel and distributed systems. However, the prevalent device-intensive nature of these systems often leads to the occurrence of faults. Tackling the critical challenge of tolerating numerous faults when constructing Hamiltonian paths and cycles in these systems is of utmost significance. The alternating group graph
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Enabling performance portability on the LiGen drug discovery pipeline Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-16 Luigi Crisci, Lorenzo Carpentieri, Biagio Cosenza, Gianmarco Accordi, Davide Gadioli, Emanuele Vitali, Gianluca Palermo, Andrea Rosario Beccari
In recent years, there has been a growing interest in developing high-performance implementations of drug discovery processing software. To target modern GPU architectures, such applications are mostly written in proprietary languages such as CUDA or HIP. However, with the increasing heterogeneity of modern HPC systems and the availability of accelerators from multiple hardware vendors, it has become
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Blockchain-based cooperative game bilateral matching architecture for shared storage Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Guanjie Lin, Mingyuan Zeng, Zhiguang Shan, Kaishun Wu, Guan Wang, Kai Lei
The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered
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Online RL-based cloud autoscaling for scientific workflows: Evaluation of Q-Learning and SARSA Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-15 Yisel Garí, Elina Pacini, Luciano Robino, Cristian Mateos, David A. Monge
Q-Learning and SARSA are two well-known reinforcement learning (RL) algorithms that have shown promising results in several application domains. However, their approach to build solutions is quite different. For example, SARSA tends to be more conservative than Q-Learning while exploring the solution space. Motivated by such differences, in this paper, we conducted an evaluation of both algorithms
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CANDIL: A federated data fabric for network analytics Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-13 Ignacio D. Martinez-Casanueva, Luis Bellido, Daniel González-Sánchez, Diego Lopez
The availability of data sources during the Big Data era provides the opportunity for new analytical applications in the networking domain, which are envisioned as one of the main enablers of the future autonomous networks. But the proliferation of heterogeneous data sources has resulted into a sea of data silos, in which finding data, understanding data, and dealing with the complexities of each data
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Enhancing federated learning robustness through randomization and mixture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-13 Seyedsina Nabavirazavi, Rahim Taheri, Sundararaja Sitharama Iyengar
Protecting data privacy is a significant challenge in machine learning (ML), and federated learning (FL) has emerged as a decentralized learning solution to address this issue. However, FL is vulnerable to poisoning attacks, which control and interrupt the learning process to substantially increase the error rate of the system. The aggregation algorithm’s robustness is crucial to prevent such attacks;
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Principled and automated system of systems composition using an ontological architecture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-10 Abdessalam Elhabbash, Yehia Elkhatib, Vatsala Nundloll, Vicent Sanz Marco, Gordon S. Blair
A distributed system’s functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity
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Quantum particle swarm optimization algorithm based on diversity migration strategy Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Chen Gong, Nanrun Zhou, Shuhua Xia, Shuiyuan Huang
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Pro-active component image placement in Edge computing environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-09 Antonios Makris, Evangelos Psomakelis, Emanuele Carlini, Matteo Mordacchini, Theodoros Theodoropoulos, Patrizio Dazzi, Konstantinos Tserpes
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A survey on blockchain technology in the maritime industry: Challenges and future perspectives Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Mohamed Ben Farah, Yussuf Ahmed, Haithem Mahmoud, Syed Attique Shah, M. Omar Al-kadri, Sandy Taramonli, Xavier Bellekens, Raouf Abozariba, Moad Idrissi, Adel Aneiba
Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges
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Multi-objective optimization-based workflow scheduling for applications with data locality and deadline constraints in geo-distributed clouds Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Dongkuo Wu, Xingwei Wang, Xueyi Wang, Min Huang, Rongfei Zeng, Kaiqi Yang
Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow
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An assignment mechanism for workflow scheduling in Function as a Service edge environment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Samaneh Hajy Mahdizadeh, Saeid Abrishami
Serverless computing has revolutionized cloud-based software development for software developers, addressing many of the associated challenges. With resource management and infrastructure provisioning handled by the provider, developers can focus on deploying services at the application level, which has gained significant popularity. Edge computing, with its proximity to end-users and ability to offer
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A sustainable smart IoT-based solid waste management system Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Amira Henaien, Hadda Ben Elhadj, Lamia Chaari Fourati
In this paper, we present a sustainable Smart City Solid Waste Management System (SCSWMS) that integrates trending technologies such as Internet of Things (IoT), Low Power Wide Area Networks (LPWANs), and Intelligent Traffic Systems (ITS) to improve solid garbage management from its inception through disposal. The Proposed SCSWMS involves three main subsystems: Smart Garbage Bins (SGBs), Smart Garbage
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Enabling DevOps for Fog Applications in the Smart Manufacturing domain: A Model-Driven based Platform Engineering approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Julen Cuadra, Ekaitz Hurtado, Isabel Sarachaga, Elisabet Estévez, Oskar Casquero, Aintzane Armentia
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Towards energy and QoS aware dynamic VM consolidation in a multi-resource cloud Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-08 Sounak Banerjee, Sarbani Roy, Sunirmal Khatua
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Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Satveer Singh, Eht E Sham, Deo Prakash Vidyarthi
Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability
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Mobility-aware personalized handover function provisioning system in B5G networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Haneul Ko, Yeunwoong Kyung, Jaewook Lee, Sangheon Pack, Namseok Ko
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Potential-based reward shaping using state–space segmentation for efficiency in reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-07 Melis İlayda Bal, Hüseyin Aydın, Cem İyigün, Faruk Polat
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An automated framework for selectively tolerating SDC errors based on rigorous instruction-level vulnerability assessment Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hussien Al-haj Ahmad, Yasser Sedaghat
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Faster or Cheaper: A Q-learning based cost-effective mixed cluster scaling method for achieving low tail latencies Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-06 Hao Yang, Li Pan, Shijun Liu
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kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud–edge environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-04 Juan Marcelo Parra-Ullauri, Hari Madhukumar, Adrian-Cristian Nicolaescu, Xunzheng Zhang, Anderson Bravalheri, Rasheed Hussain, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou
Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given
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A new approach to Mergesort algorithm: Divide smart and conquer Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-03 Sahin Emrah Amrahov, Yilmaz Ar, Bulent Tugrul, Bekir Emirhan Akay, Nermin Kartli
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GenArchBench: A genomics benchmark suite for arm HPC processors Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Lorién López-Villellas, Rubén Langarita-Benítez, Asaf Badouh, Víctor Soria-Pardos, Quim Aguado-Puig, Guillem López-Paradís, Max Doblas, Javier Setoain, Chulho Kim, Makoto Ono, Adrià Armejach, Santiago Marco-Sola, Jesús Alastruey-Benedé, Pablo Ibáñez, Miquel Moretó
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Edge model: An efficient method to identify and reduce the effectiveness of malicious clients in federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Mahdi shahraki, Amir Jalaly Bidgoly
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CoTwin: Collaborative improvement of digital twins enabled by blockchain Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-02 Marisol García-Valls, Alejandro M. Chirivella-Ciruelos
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An efficient cloud-integrated distributed deep neural network framework for IoT malware classification Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-01 Mohammad Reza Babaei Mosleh, Saeed Sharifian
The proliferation of interconnected devices in the Internet of Things (IoT) landscape has introduced significant security concerns. With the integration of android devices, the potential for attackers to exploit vulnerabilities becomes a crucial issue. Timely detection of malware attacks has emerged as a critical challenge, especially in industrial IoT (IIoT), to prevent permanent damage. The slow
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Lightweight block ciphers for resource-constrained environments: A comprehensive survey Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-01 Yue Zhong, Jieming Gu
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Exploiting microservices and serverless for Digital Twins in the cloud-to-edge continuum Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-04-01 Paolo Bellavista, Nicola Bicocchi, Mattia Fogli, Carlo Giannelli, Marco Mamei, Marco Picone
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Efficient decentralized optimization for edge-enabled smart manufacturing: A federated learning-based framework Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-30 Huan Liu, Shiyong Li, Wenzhe Li, Wei Sun
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A scalable multi-density clustering approach to detect city hotspots in a smart city Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-28 Eugenio Cesario, Paolo Lindia, Andrea Vinci
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QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-27 Nathaniel Hudson, Hana Khamfroush, Matt Baughman, Daniel E. Lucani, Kyle Chard, Ian Foster
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Enabling privacy-aware interoperable and quality IoT data sharing with context Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-27 Tek Raj Chhetri, Chinmaya Kumar Dehury, Blesson Varghese, Anna Fensel, Satish Narayana Srirama, Rance J. DeLong
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A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-26 Zihui Sun, Anfeng Liu, Neal N. Xiong, Qian He, Shaobo Zhang
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Accelerating range minimum queries with ray tracing cores Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-26 Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada, Felipe A. Quezada
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LPP-BPSI: A location privacy-preserving scheme using blockchain and Private Set Intersection in spatial crowdsourcing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Libo Feng, Yifan Liu, Kai Hu, Xue Zeng, Fake Fang, Jiale Xie, Shaowen Yao
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NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Samir Si-Mohammed, Anthony Bardou, Thomas Begin, Isabelle Guérin Lassous, Pascale Vicat-Blanc
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Validity constraints for data analysis workflows Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-25 Florian Schintke, Khalid Belhajjame, Ninon De Mecquenem, David Frantz, Vanessa Emanuela Guarino, Marcus Hilbrich, Fabian Lehmann, Paolo Missier, Rebecca Sattler, Jan Arne Sparka, Daniel T. Speckhard, Hermann Stolte, Anh Duc Vu, Ulf Leser
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Fluidity: Providing flexible deployment and adaptation policy experimentation for serverless and distributed applications spanning cloud–edge–mobile environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Foivos Pournaropoulos, Alexandros Patras, Christos D. Antonopoulos, Nikos Bellas, Spyros Lalis
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MiniPFL: Mini federations for hierarchical personalized federated learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Yuwei Fan, Wei Xi, Hengyi Zhu, Jizhong Zhao
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Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-21 Gustavo André Setti Cassel, Rodrigo da Rosa Righi, Cristiano André da Costa, Marta Rosecler Bez, Marcelo Pasin
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BDPM: A secure batch dynamic password management scheme in industrial internet environments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-20 Jingyu Feng, Rui Yan, Gang Han, Wenbo Zhang
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A Big Data architecture for early identification and categorization of dark web sites Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-20 Javier Pastor-Galindo, Hông-Ân Sandlin, Félix Gómez Mármol, Gérôme Bovet, Gregorio Martínez Pérez
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Grassroots operator search for model edge adaptation using mathematical search space Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-19 Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar
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Enhancing generalization in Federated Learning with heterogeneous data: A comparative literature review Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-18 Alessio Mora, Armir Bujari, Paolo Bellavista
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LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-18 Kun Gong, Senlin Luo, Limin Pan, Linghao Zhang, Yifei Zhang, Haomiao Yu
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Coarse-to-Fine: A hierarchical DNN inference framework for edge computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-16 Zao Zhang, Yuning Zhang, Wei Bao, Changyang Li, Dong Yuan
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A transferred spatio-temporal deep model based on multi-LSTM auto-encoder for air pollution time series missing value imputation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-15 Xiaoxia Zhang, Pengcheng Zhou
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Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-15 Yuncan Tang, Yongquan Liang, Yang Liu, Jinquan Zhang, Lina Ni, Liang Qi
Federated learning, a promising distributed machine learning paradigm, has been used in various Internet of Things (IoT) environments to solve isolated data island issues and protect data privacy. However, since the central server in federated learning cannot detect the local training process of the client, it is vulnerable to adversarial attacks against its security and privacy by malicious clients
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Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Hameedur Rahman, Uzair Muzamil Shah, Syed Morsleen Riaz, Kashif Kifayat, Syed Atif Moqurrab, Joon Yoo
Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development
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Software Quality Assurance as a Service: Encompassing the quality assessment of software and services Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Samuel Bernardo, Pablo Orviz, Mario David, Jorge Gomes, David Arce, Diana Naranjo, Ignacio Blanquer, Isabel Campos, Germán Moltó, Joao Pina
This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices
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Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Chenshan Ren, Wei Song, Xinchen Lyu
By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the
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Multi-GPU work sharing in a task-based dataflow programming model Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Joseph John, Josh Milthorpe, Thomas Herault, George Bosilca
Today, multi-GPU computing nodes are the mainstay of most high-performance computing systems. Despite significant progress in programmability, building an application that efficiently utilizes all the GPUs in a computing node is still a significant challenge, especially using the existing shared-memory and message-passing paradigms. In this aspect, the task-based dataflow programming model has emerged
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PPSFL: Privacy-Preserving Split Federated Learning for heterogeneous data in edge-based Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Jiali Zheng, Yixin Chen, Qijia Lai
With the rapid increase in the number of Internet of Things (IoT) devices and the amount of data they generate, the traditional cloud-based approach is gradually unable to meet the actual needs of many scenarios. Distributed collaborative machine learning (DCML) paradigms such as Federated Learning (FL) and Split Learning (SL) provide possibilities for effective use of decentralized data in edge-based
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Uncertainty-aware autonomous sensing with deep reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics