Energy-aware federated learning for secure edge computing in 5G-enabled IoT networks
Bibliographic record
Abstract
Abstract The rapid expansion of 5G-enabled IoT networks has intensified the need for efficient, secure, and privacy-preserving machine learning models that can operate in decentralized edge environments. Federated learning (FL) has emerged as a promising solution by enabling collaborative training without sharing raw data. However, traditional FL implementations suffer from excessive energy consumption, vulnerability to adversarial attacks, and inefficient resource utilization in heterogeneous edge computing infrastructures. To address these challenges, we propose an energy-aware federated learning (EAFL) framework, integrating adaptive client selection, quantization-aware model updates, and blockchain-enhanced security mechanisms to improve both energy efficiency and resistance to model poisoning attacks and adversarial gradient manipulations. Our method dynamically selects participating IoT devices based on energy constraints and computational capacity, reducing unnecessary communication overhead. Additionally, quantization-aware training minimizes computational complexity, while blockchain-based security enhancements protect against data manipulation and adversarial model poisoning attacks. We evaluate the EAFL framework using benchmark IoT datasets and simulated 5G edge environments, demonstrating a 35.4% reduction in energy consumption while maintaining a high model accuracy of 91.8%. Furthermore, our blockchain-integrated security mechanism reduces model poisoning attack success rates by 72.3%, outperforming conventional FL approaches. This study provides a novel interdisciplinary contribution at the intersection of privacy-preserving AI, energy-efficient edge computing, and decentralized security architectures, paving the way for more sustainable and secure IoT applications in smart healthcare, autonomous systems, and industrial automation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".