FedSL: A Communication-Efficient Federated Learning With Split Layer Aggregation DOI
Weishan Zhang, Tao Zhou, Qinghua Lu

и другие.

IEEE Internet of Things Journal, Год журнала: 2024, Номер 11(9), С. 15587 - 15601

Опубликована: Янв. 5, 2024

Federated learning (FL) can train a model collaboratively through multiple remote clients without sharing raw data. The challenge of federated is how to decrease network transmissions. This article aims reduce traffic by transmitting fewer neural parameters. We first investigate similarities different corresponding layers convolutional (CNN) models in FL, and find that there lot redundant information its feature extractors. For this, we propose communication-efficient aggregation algorithm named FedSL (Federated Split Layers) the communication overhead. Based on number global layers, divides client into groups depth dimension. A Max-Min selection strategy employed select participants for each layer. Each only transfers partial parameters those are selected, which reduces aggregates group concatenates all according order layers. experimental results demonstrate improves efficiency compared algorithms (e.g., FedAvg, FedProx, MOON), decreasing 42% cost with VGG-style CNN 70% ResNet-9, while maintaining similar accuracy baseline algorithms.

Язык: Английский

A Survey of Explainable Artificial Intelligence for Smart Cities DOI Open Access
Abdul Rehman Javed, Waqas Ahmed, Sharnil Pandya

и другие.

Electronics, Год журнала: 2023, Номер 12(4), С. 1020 - 1020

Опубликована: Фев. 18, 2023

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives humans and envisioned concept smart cities using informed actions, user interpretations explanations, firm decision-making processes. XAI systems can unbox potential black-box AI models describe them explicitly. study comprehensively surveys current future developments in technologies for cities. It also highlights societal, industrial, technological trends that initiate drive towards presents key to enabling detail. paper discusses cities, various technology use cases, challenges, applications, possible alternative solutions, research enhancements. Research projects activities, including standardization efforts toward developing are outlined lessons learned from state-of-the-art summarized, technical challenges discussed shed new light on possibilities. presented is a first-of-its-kind, rigorous, detailed assist researchers implementing XAI-driven systems, architectures, applications

Язык: Английский

Процитировано

120
AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives DOI
Guneet Kaur Walia, Mohit Kumar, Sukhpal Singh Gill

и другие.

IEEE Communications Surveys & Tutorials, Год журнала: 2023, Номер 26(1), С. 619 - 669

Опубликована: Ноя. 30, 2023

The proliferation of ubiquitous Internet Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation agriculture are giving rise to a prodigious amount data requiring everincreasing computations services from cloud the edge network.Fog/Edge computing is promising distributed paradigm that has drawn extensive attention both industry academia.The infrastructural efficiency these paradigms necessitates adaptive resource management mechanisms for offloading decisions efficient scheduling.Resource Management (RM) non-trivial issue whose complexity result heterogeneous resources, incoming transactional workload, node discovery, Quality Service (QoS) parameters at same time, which makes efficacy resources even more challenging.Hence, researchers have adopted Artificial Intelligence (AI)-based techniques resolve abovementioned issues.This paper offers comprehensive review issues challenges Fog/Edge by categorizing them into provisioning task offloading, scheduling, service placement, load balancing.In addition, existing AI non-AI based state-of-the-art solutions been discussed, along with their QoS metrics, datasets analysed, limitations challenges.The survey provides mathematical formulation corresponding each categorized issue.Our work sheds light on research directions cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum Software-Defined Networking (SDN), can be integrated frameworks fog/edge-of-things improve business intelligence analytics amongst IoT-based applications.

Язык: Английский

Процитировано

97
Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review DOI
Noor Ul Huda, Ijaz Ahmed,

Muhammad Adnan

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122380 - 122380

Опубликована: Ноя. 1, 2023

Язык: Английский

Процитировано

86
A systematic review of federated learning: Challenges, aggregation methods, and development tools DOI
Souhila Badra Guendouzi, Samir Ouchani,

Hiba EL Assaad

и другие.

Journal of Network and Computer Applications, Год журнала: 2023, Номер 220, С. 103714 - 103714

Опубликована: Авг. 10, 2023

Язык: Английский

Процитировано

60
Unlocking the Future: Fostering Human–Machine Collaboration and Driving Intelligent Automation through Industry 5.0 in Smart Cities DOI Creative Commons
Amr Adel

Smart Cities, Год журнала: 2023, Номер 6(5), С. 2742 - 2782

Опубликована: Окт. 10, 2023

In the quest to meet escalating demands of citizens, future smart cities emerge as crucial entities. Their role becomes even more vital given current challenges posed by rapid urbanization and need for sustainable inclusive living spaces. At heart these are advancements in information communication technologies, with Industry 5.0 playing an increasingly significant role. This paper endeavors conduct exhaustive survey analyze including potential their implications cities. The crux is exploration technological across various domains that set shape urban environments. discussion spans diverse areas but not limited cyber–physical systems, fog computing, unmanned aerial vehicles, renewable energy, machine learning, deep cybersecurity, digital forensics. Additionally, sheds light on specific city context, illuminating its impact enabling advanced cybersecurity measures, fostering human–machine collaboration, driving intelligent automation services, refining data management decision making. also offers in-depth review existing frameworks shaping applications, evaluating how technologies could augment frameworks. particular, delves into face, bringing 5.0-enabled solutions fore.

Язык: Английский

Процитировано

60
A Survey on Role of Blockchain for IoT: Applications and Technical Aspects DOI

Shikha Mathur,

Anshuman Kalla, Gürkan Gür

и другие.

Computer Networks, Год журнала: 2023, Номер 227, С. 109726 - 109726

Опубликована: Март 21, 2023

Язык: Английский

Процитировано

55
A decade of research in fog computing: Relevance, challenges, and future directions DOI
Satish Narayana Srirama

Software Practice and Experience, Год журнала: 2023, Номер 54(1), С. 3 - 23

Опубликована: Июль 18, 2023

Abstract Recent developments in the Internet of Things (IoT) and real‐time applications, have led to unprecedented growth connected devices their generated data. Traditionally, this sensor data is transferred processed at cloud, control signals are sent back relevant actuators, as part IoT applications. This cloud‐centric model, resulted increased latencies network load, compromised privacy. To address these problems, Fog Computing was coined by Cisco 2012, a decade ago, which utilizes proximal computational resources for processing Ever since its proposal, fog computing has attracted significant attention research fraternity focused addressing different challenges such frameworks, simulators, resource management, placement strategies, quality service aspects, economics so forth. However, after research, we still do not see large‐scale deployments public/private networks, can be utilized realizing interesting In literature, only pilot case studies small‐scale testbeds, utilization simulators demonstrating scale specified models respective technical challenges. There several reasons this, most importantly, did present clear business companies participating individuals yet. article summarizes technical, non‐functional, economic challenges, been posing hurdles adopting computing, consolidating them across clusters. The also academic industrial contributions provides future directions considering emerging trends federated learning quantum computing.

Язык: Английский

Процитировано

52
Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning DOI
Nawaf Abdulla, Mehmet Demirci, Suat Özdemi̇r

и другие.

Sustainable Energy Grids and Networks, Год журнала: 2024, Номер 38, С. 101342 - 101342

Опубликована: Март 11, 2024

Язык: Английский

Процитировано

23
Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions DOI Creative Commons
Dalhatu Muhammed, Ehsan Ahvar, Shohreh Ahvar

и другие.

Journal of Network and Computer Applications, Год журнала: 2024, Номер 228, С. 103905 - 103905

Опубликована: Июнь 1, 2024

The Artificial Intelligence of Things (AIoT), a combination the Internet (IoT) and (AI), plays an increasingly important role in smart agriculture (SA). AIoT has been adopted many applications including agriculture, such as crop yield estimation, soil water conservation, pest disease detection supply chain management. While there are plenty studies on healthcare, cities, manufacturing, transportation, SA still small share reported research. This paper presents comprehensive review existing literature Federated Learning (FL) for SA. It identifies current potential challenges provides research direction future investment both academia industry.

Язык: Английский

Процитировано

22
Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications DOI Creative Commons
Ηλίας Δρίτσας, Μαρία Τρίγκα

Journal of Sensor and Actuator Networks, Год журнала: 2025, Номер 14(1), С. 9 - 9

Опубликована: Янв. 22, 2025

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine (ML), addressing the unique demands of Internet Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides comprehensive overview FL, focusing on its integration with IoT. We delve into motivations behind adopting FL IoT, underlying techniques that facilitate this integration, challenges posed by IoT environments, diverse range applications is making an impact. Finally, submission also outlines future research directions open issues, aiming to provide detailed roadmap advancing in settings.

Язык: Английский

Процитировано

6