Adaptive Neuro-Federated Distributed Computing Framework for Intelligent Edge-Cloud Collaboration in Real-Time Applications
Bibliographic record
Abstract
The present work describes a neuro-federated distributed computing framework which adapts to perform real-time intelligent edge-cloud cooperation in computing applications. LSTM networks operate at edge devices for model training purposes while the framework distributes updates through Federated Averaging which avoids exposing raw data to other nodes. The system achieves evaluation through four essential performance criteria including accuracy of models and time to convergence and communication efficiency as well as resource usage metrics. The global model surpasses individual local models in performance following several rounds of communication thereby proving the effectiveness of federated learning for performance improvement. The system demonstrates increased operational speed together with reduced information transfer amounts and reduced system resource requirements during training stages because of its high efficiency. The developed framework delivers an extendable privacy-protecting technique which serves real-time needs in various business sectors such as healthcare and IoT and autonomous systems. Federated learning united with LSTM demonstrates great potential for resolving distributed machine learning issues that occur in edge-cloud systems according to our research findings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".