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 4
Special Issue Editors
Interests: clustering; multi-view learning; federated learning
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 100 words) can be sent to the Editorial Office for announcement on this website.
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 1600 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
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