Distributed Machine Learning and Federated Edge Computing for IoT
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 22
Special Issue Editors
Interests: distributed and internet computing; big data analytics; cloud and edge computing
Interests: data management; big data; data mining; databases; privacy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Internet of Things (IoT) is prevailing as the dominating service paradigm shaping how we interact with Internet-enabled physical devices in the comfort of our homes, workplaces, and communities, and this shift can be attributed to the myriad technological advancements witnessed in recent times. With an abundance of IoT devices generating vast amounts of decentralized data, the intersection of Distributed Machine Learning and Federated Edge Computing is becoming a key driver for empowering IoT services to facilitate the transfer of scalable and privacy-preserving intelligence across network extremes and on any device augmented with an Internet connection.
This Special Issue aims to address the challenges and explore new directions in deploying and optimizing machine learning algorithms across distributed networks and edge computing deployments, ensuring systems are scalable, privacy-preserving, cost-efficient, environmentally sustainable, and at the same time, provide decentralized intelligence across the IoT–edge–cloud continuum.
Facilitating this vision, we encourage submissions that address the following topics:
- Distributed Machine Learning algorithms, frameworks, and optimization techniques introducing efficiencies in QoS, performance, and/or costs.
- Federated Learning and Split Learning techniques, algorithms, and/or cross-silo architectures presenting innovations in client selection, communication and global aggregation, privacy preservation, and bias mitigation to enhance model performance and fairness.
- Energy efficiency and carbon-conscious techniques to support sustainable AI-powered IoT and edge computing services by utilizing Distributed Machine Learning.
- Benchmarking, monitoring, and testbeds for Distributed Machine Learning and Federated Edge Computing.
- Novel IoT Applications and/or empirical studies introducing Distributed Machine Learning and Federated Edge Computing.
Dr. Demetris Trihinas
Dr. Alexandros Karakasidis
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
- machine learning
- federated learning
- split learning
- internet of things
- edge computing
- energy-aware machine learning
- distributed artificial intelligence
- data privacy
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