Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
3.6
Journals
Future Internet
Special Issues
Clustered Federated Learning for Networks
share announcement

Clustered Federated Learning for Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 2005

Special Issue Editors

Dr. Kristina P. Sinaga

E-Mail Website
Guest Editor
Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Via G. Moruzzi, 1, 56124 Pisa, Italy
Interests: clustering; multi-view learning; federated learning
Prof. Dr. Miin-shen Yang

E-Mail Website
Guest Editor
Department of Applied Mathematics, Chung Yuan Christian University, Taoyuan 32023, Taiwan
Interests: fuzzy clustering; machine learning and pattern recognition; image segmentation; industrial systems; statistic applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Future Internet, “Clustered Federated Learning for Networks,” aims to gather cutting-edge research on federated learning, with a particular emphasis on clustering techniques and their application in networked systems.

As privacy concerns and data regulations become increasingly stringent, federated learning emerges as a crucial paradigm that enables collaborative machine learning without compromising data privacy. Clustering techniques further enhance this capacity by improving efficiency, reducing communication costs, and enabling personalized models for heterogeneous participants.

This Issue will explore clustered federated learning, a method that boosts the efficiency and scalability of federated systems by grouping participants with similar characteristics.

We invite researchers and practitioners to submit original research articles, comprehensive reviews, and insightful case studies that tackle current challenges, propose innovative solutions, and outline future research directions in clustered federated learning for networks. We invite submissions covering the following areas (but not limited to) the following areas:

  • Algorithmic Innovations
  • Novel clustering algorithms for federated learning
  • Adaptive clustering strategies
  • Hierarchical federated learning architectures
  • Privacy & Security
  • Privacy-preserving clustering mechanisms
  • Differential privacy in clustered FL
  • Secure aggregation protocols
  • System Optimization
  • Communication-efficient algorithms
  • Resource allocation and scheduling
  • Heterogeneity handling strategies
  • Network Applications
  • IoT and edge computing scenarios
  • Mobile and wireless networks
  • Multi-access edge computing (MEC)
  • Performance Analysis
  • Convergence analysis and guarantees
  • Robustness and fault tolerance
  • Scalability studies

Dr. Kristina P. Sinaga
Prof. Dr. Miin-shen Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clustered federated learning
  • federated learning
  • machine learning clustering
  • decentralized learning
  • networked systems
  • IoT and edge computing
  • multi-view learning
  • network optimization
  • privacy-preserving AI
  • scalable machine learning
  • distributed algorithms
  • communication efficiency
  • heterogeneous networks
  • secure aggregation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 1380 KB  
Article
Selection of Optimal Cluster Head Using MOPSO and Decision Tree for Cluster-Oriented Wireless Sensor Networks
by Rahul Mishra, Sudhanshu Kumar Jha, Shiv Prakash and Rajkumar Singh Rathore
Future Internet 2025, 17(12), 577; https://doi.org/10.3390/fi17120577 - 15 Dec 2025
Viewed by 356
Abstract
Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and [...] Read more.
Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article
(This article belongs to the Special Issue Clustered Federated Learning for Networks)
►▼ Show Figures

Figure 1

29 pages, 3021 KB  
Article
Fog-Aware Hierarchical Autoencoder with Density-Based Clustering for AI-Driven Threat Detection in Smart Farming IoT Systems
by Manikandan Thirumalaisamy, Sumendra Yogarayan, Md Shohel Sayeed, Siti Fatimah Abdul Razak and Ramesh Shunmugam
Future Internet 2025, 17(12), 567; https://doi.org/10.3390/fi17120567 - 10 Dec 2025
Viewed by 382
Abstract
Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly [...] Read more.
Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article
(This article belongs to the Special Issue Clustered Federated Learning for Networks)
►▼ Show Figures

Figure 1

21 pages, 2519 KB  
Article
Efficient Lightweight Image Classification via Coordinate Attention and Channel Pruning for Resource-Constrained Systems
by Yao-Liang Chung
Future Internet 2025, 17(11), 489; https://doi.org/10.3390/fi17110489 - 25 Oct 2025
Viewed by 934
Abstract
Image classification is central to computer vision, supporting applications from autonomous driving to medical imaging, yet state-of-the-art convolutional neural networks remain constrained by heavy floating-point operations (FLOPs) and parameter counts on edge devices. To address this accuracy–efficiency trade-off, we propose a unified lightweight [...] Read more.
Image classification is central to computer vision, supporting applications from autonomous driving to medical imaging, yet state-of-the-art convolutional neural networks remain constrained by heavy floating-point operations (FLOPs) and parameter counts on edge devices. To address this accuracy–efficiency trade-off, we propose a unified lightweight framework built on a pruning-aware coordinate attention block (PACB). PACB integrates coordinate attention (CA) with L1-regularized channel pruning, enriching feature representation while enabling structured compression. Applied to MobileNetV3 and RepVGG, the framework achieves substantial efficiency gains. On GTSRB, MobileNetV3 parameters drop from 16.239 M to 9.871 M (–6.37 M) and FLOPs from 11.297 M to 8.552 M (–24.3%), with accuracy improving from 97.09% to 97.37%. For RepVGG, parameters fall from 7.683 M to 7.093 M (–0.59 M) and FLOPs from 31.264 M to 27.918 M (–3.35 M), with only ~0.51% average accuracy loss across CIFAR-10, Fashion-MNIST, and GTSRB. Complexity analysis further confirms PACB does not increase asymptotic order, since the additional CA operations contribute only lightweight lower-order terms. These results demonstrate that coupling CA with structured pruning yields a scalable accuracy–efficiency trade-off under hardware-agnostic metrics, making PACB a promising, deployment-ready solution for mobile and edge applications. Full article
(This article belongs to the Special Issue Clustered Federated Learning for Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop