Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated remarkable success in solving complex problems in different applications. In recent years, new solutions have been developed based on this combined framework (i.e., federated reinforcement learning). Conversely, there is a lack of analysis concerning IoT applications and a standard view of challenges and future directions of the current FRL landscape. Thereupon, the main goal of this research is to present a literature review of federated reinforcement learning (FRL) applications in IoT from multiple perspectives. We focus on analyzing applications in multiple areas (e.g., security, sustainability and efficiency, vehicular solutions, and industrial services) to highlight existing solutions, their characteristics, and research gaps. Additionally, we identify key short- and long-term challenges leading to new opportunities in the field. This research intends to picture the current FRL ecosystem in IoT to foster the development of new solutions based on existing challenges.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.014 | 0.053 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it