MultiMOORA-Guided Federated Learning With KD Stabilization for Health Monitoring Devices as Protection Against Poisoning Attacks
Bibliographic record
Abstract
Federated learning (FL) has emerged as a practical solution for training deep neural networks while preserving data privacy, particularly in scenarios like the Internet of Things (IoT), where user devices generate sensitive private data. While FL addresses privacy concerns, existing aggregation methods remain vulnerable to malicious poisoning attacks. Therefore, developing new approaches to optimize client selection as a preventive mechanism is crucial to FL’s strategy. This paper introduces a novel enhancement to FL by integrating the MultiMOORA technique into a robust aggregation mechanism with knowledge distillation (KD). During FL rounds, local models are evaluated using classification metrics (loss, accuracy, precision, recall, f1-score) and ranked using the Multi-Criteria Decision Maker (MCDM). The resulting ranking is used to select the best-performing models for aggregation and detect possible infected clients. Furthermore, the top client is chosen as an aggregator. During aggregation, chosen best models are weight-averaged and form teacher ensemble, used for stabilizing newly formed global model via KD. The proposed FL strategy reduces the risk of adversarial poisoning attacks on FL systems and stabilizes FL training process. The experimental results indicate the system’s resilience and ability to perform reliably in hostile environments, as the proposed FL system achieved 89.50% F1-score under label-poisoning scenario. Based on further analysis, the proposed methods could be integrated into a digital-twin-based modern healthcare system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".