Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust
Bibliographic record
Abstract
Recent advancements in Internet of Things (IoT) and edge computing have led to rapid growth in the number of IoT devices generating extensive volumes of data at the network edge. Efficiently scheduling tasks on these devices, particularly under strict latency constraints in federated learning (FL) environments, poses substantial challenges. In this paper, we propose a novel trust-energy-aware scheduling framework specifically designed for latency-constrained federated edge computing scenarios. Our innovative strategy integrates Dynamic Deep Q-Network (Dynamic-DQN) reinforcement learning with Local Interpretable Model-agnostic Explanations (LIME), enabling dynamic, real-time assessment of device trustworthiness with interpretability and transparency. This combined approach allows the framework to intelligently allocate tasks to IoT devices, explicitly optimizing for reduced latency, improved energy efficiency, and enhanced system reliability. Extensive experimental evaluations confirm that our proposed method substantially outperforms conventional reinforcement learning and heuristic scheduling algorithms, demonstrating significant reductions in latency, superior energy management, and improved scalability. These results underscore the robustness and practical effectiveness of our framework in addressing critical FL challenges. • Our dynamic-DQN policy integrates trust and energy for IoT task scheduling. • The solution evaluates the devices trustworthiness based on interpretable features. • The framework optimizes task allocation in federated edge computing environments. • The experimental results demonstrated improvement in task scheduling performance. • We contribute to resource efficiency and cost optimization in IoT edge computing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".