Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) show significant potential through their ability to collect and analyze real-time data, notably enhancing various sectors. The new emerging security threats present a severe risk to the security and reliability of WSNs. Data-driven Artificial Intelligence (AI) leverages WSNs data to deal with new emerging threats like zero-day attacks. However, AI-based models suffer from poor adoption due to the lack of realistic/up-to-date attack data. Recently, Multi-Agent Deep Reinforcement Learning (MARL) has gained significant attention for enhancing Intrusion Detection Systems (IDS) capabilities. MARL offers improved flexibility, efficiency, and robustness. However, this requires data sharing, leading to network bandwidth consumption and slower training. Additionally, the curse of dimensionality hampers its benefits, given the exponential expansion of the state-action space. Privacy-aware collaborative methods such as Federated Learning (FL) emerge as a new approach, enabling decentralized model training across a network of devices while preserving the privacy of each participant. In this context, we introduce a novel framework (MAF-DRL) that leverages FL and MARL to efficiently detect WSN-based attacks. MAF-DRL enables distributed learning across multiple agents with adaptive, flexible, and robust attack detection. We also introduce a trust-based scheduling mechanism that dynamically allocates resources based on agent reliability. This trust-aware approach allows FL systems to adapt to changing network conditions and device behaviors. By prioritizing reliable devices, our method improves the energy efficiency of WSNs and enhances the resilience and effectiveness of the distributed FL paradigm. Finally, we assess the robustness of our framework by testing it against real-world WSN attacks. This evaluation demonstrates its efficiency for secure and communication-efficient federated edge learning across various agents.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".