Empowering IIoT With Federated Edge Learning for Human Activity Recognition Problems
Bibliographic record
Abstract
Human Activity Recognition (HAR) has become a cornerstone in the dynamic development of the Industrial Internet of Things (IIoT). This study introduces an extensive framework aimed at embedding HAR functionality into industrial settings to promote workplace safety, streamline operations, and facilitate predictive maintenance. Through AI techniques, HAR problems can be achieved with high accuracy. However, traditional AI models require centrally trained data on remotely powerful cloud servers. This leads to issues with privacy and security of health records and increases latency. To address this problem, the Federated Learning (FL) technique was proposed. FL allows distributed training on the patient’s IoT devices and serves as the communication mechanism between local devices and the FL aggregator. Thanks to this architecture, the health data needs only to be stored locally on its devices without being uploaded to data centers, thus ensuring security and reducing service response times and computational costs. In this study, we implement FedANN and FedConvNN independently in a federated learning setting to address human activity recognition problems toward real-time applications. Finally, we evaluate the effectiveness of the based on the variation initiation of the number of different training clients. The results show that the FedConvNN solution improves accuracy and reduces model and communication complexity compared to FedANN and centralized training models, with the potential for real-time deployment in HAR tasks. Our code is available on our GitHub repository: https://github.com/itsminhcs/Fedavg-HAR.git.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".