Symbiotic Federated Learning for Giant AI Threat Detection in 6G-IoT Infrastructures
Bibliographic record
Abstract
The increasing demand for intelligent, privacy-aware, and scalable solutions at the edge of the network is accelerating the convergence of Giant AI models and Internet of Things (IoT) infrastructures in 6G environments. In this article, we propose a symbiotic threat detection framework that unifies federated learning (FL), graph neural networks (GNNs), and HE to enable decentralized anomaly detection across distributed 6G-enabled IoT ecosystems. Our approach addresses key challenges in current cloud-centric architectures, including data privacy, communication efficiency, and lack of interpretability in AI-driven threat detection. The proposed framework, SymFL-GNN, supports collaborative learning among IoT devices while retaining data locally, leveraging the PHC to ensure gradient-level encryption. To enhance interpretability, a dynamic sensor graph is constructed using self-learned embeddings and attention mechanisms, allowing the model to pinpoint anomalous behaviors and their sources. We evaluate our framework on two real-world industrial datasets (SWaT and WADI) representing cyber-physical water systems, achieving 96.3% and 96.0% accuracy, respectively, with significant improvements over existing baselines in both precision and F1 score. Our results show that SymFL-GNN effectively balances local autonomy with global intelligence, supporting the vision of symbiotic AI at the edge. The framework demonstrates how 6G-enabled IoT networks can jointly contribute to and benefit from Giant AI models, laying the foundation for secure, intelligent, and privacy-preserving distributed systems in critical infrastructure and consumer environments.
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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 | 0.006 |
| 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".