Federated and Distributed Intelligence for Sustainable Edge-Cloud Ecosystems

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 698

Editors


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Guest Editor
Department of Computer Technology and Computation, University of Alicante, Alicante, Spain
Interests: federated learning; edge–cloud computing; distributed AI; IoT/IoV; sustainable AI; smart cities

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Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff, UK
Interests: hierarchical federated learning; security in energy management systems; privacy-preserving green IoT; adversarial AI for smart cities; threat detection in edge–cloud ecosystems

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Guest Editor
Department of Computer Technology and Computation, University of Alicante, Alicante, Spain
Interests: green edge computing architectures; sustainable IoT paradigms; high-performance computing for urban sustainability; cloud-to-edge orchestration; smart city services
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Special Issue Information

Dear Colleagues,

The rapid expansion of edge–cloud ecosystems, IoT infrastructures, and AI-driven services is creating unprecedented pressure on energy consumption, data governance, and environmental sustainability. Federated and distributed intelligence have emerged as key paradigms to address these challenges by enabling decentralized, energy-aware, and privacy-preserving learning.

This Special Issue explores how decentralized AI methods can support sustainable digital infrastructures, aligning with SDG 9, 11, and 13. Crucially, we move beyond traditional performance metrics to emphasize the trade-offs between model accuracy and environmental impact. We welcome contributions that quantify energy consumption (carbon footprint), propose lightweight communication protocols, or demonstrate energy-efficient orchestration.

Research topics include the following: federated learning and distributed optimization; energy-efficient edge–cloud orchestration; sustainable IoT/IoV systems; privacy-preserving data governance; distributed situational awareness for smart cities; and frameworks for assessing the environmental footprint of distributed AI.

Both original research articles and reviews are welcome.

Dr. Francisco A. Pujol
Dr. Amir Javed
Prof. Dr. Higinio Mora
Guest Editors

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Keywords

  • federated learning
  • distributed intelligence
  • sustainable edge–cloud computing
  • green federated learning
  • green IoT and IoV
  • privacy-preserving machine learning
  • smart cities and mobility
  • carbon-aware computing
  • edge intelligence
  • resource-constrained AI

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Published Papers (1 paper)

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Research

28 pages, 782 KB  
Article
Federated Residual-Element Optimization for Compliance- Aware Environmental Artificial Intelligence in Distributed Effluent Monitoring
by Koffka Khan, Winston Elibox and Shanta Ramnath
AI 2026, 7(7), 253; https://doi.org/10.3390/ai7070253 - 10 Jul 2026
Viewed by 324
Abstract
Federated learning (FL) can support environmental monitoring when facilities cannot pool raw operational records, but ordinary FL does not encode permit direction, regulatory margins, censoring, missingness, or facility-specific compliance regimes. This study introduces Federated Residual-Element Optimization (FREO), a compliance-aware environmental artificial intelligence (AI) [...] Read more.
Federated learning (FL) can support environmental monitoring when facilities cannot pool raw operational records, but ordinary FL does not encode permit direction, regulatory margins, censoring, missingness, or facility-specific compliance regimes. This study introduces Federated Residual-Element Optimization (FREO), a compliance-aware environmental artificial intelligence (AI) framework that converts effluent measurements into signed, unit-consistent regulatory residuals and communicates compact residual elements rather than raw samples. Ten de-identified Environmental Management Authority effluent workbooks, treated as workbook-level facility-category clients, produced 4036 assessable observations, 321 facility-month states, and 311 next-period prediction records. A boundary-purged temporal split removed train/test target-input overlap, leaving 204 training and 97 held-out records. On the within-facility next-period high-burden task, FREO achieved AUROC 0.966, AUPRC 0.923, F1 0.852, and Brier score 0.079, with facility-month bootstrap intervals of [0.924, 0.996], [0.810, 0.990], [0.727, 0.941], and [0.056, 0.104], respectively. Matched calibrated baselines indicate that residual features and training-only facility-prior calibration explain much of the gain; FedAvg-Cal slightly exceeded FREO on AUPRC, F1, and Brier. FREO is therefore positioned as an auditable residual-element workflow for raw-data-local regulatory screening, not as a universally superior optimizer. Full article
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