Realizing Health 4.0 in Beyond 5G Networks
Bibliographic record
Abstract
The advancements of Edge and Internet of Things (IoT) devices in terms of their processing, storage and communication capabilities, in addition to the advancements in wireless communication and networking technologies, have led to the rise in Intelligent Edge-enabled IoT architectures. Federated Learning (FL) is one example in which intelligence is adapted to the edge to offload some of the processing load from centralized entities and maintain secure localized model training. With Health 4.0, it is anticipated that distributed and edge-supported Artificial Intelligence (AI) will enable faster and more accurate early-stage disease discovery that relies significantly on intelligent remote and on-site IoT devices. Given that healthcare systems are highly scrutinized by both governments and patients to maintain high levels of data privacy and security, FL coupled with the support of blockchain will provide an optimal solution to reinforce today's healthcare frameworks. In this paper, we propose a FL-enabled framework for healthcare systems that is supported by edge-computing, blockchain and intelligent IoT devices. The solution considers a pneumonia detection use-case as a proof-of-concept and is applicable to an extended set of health-related use-cases. Different pre-trained models are compared against the proposed FL-supported model, namely, CNN, GG16, VGG19, InceptionV3, ResNet, DenseNet, and Xception. Results show high model accuracy attainment and significant improvements in terms of data privacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".