xG Security: Zero-Trust and Moving Target Defense in Decentralized Learning Environment
Bibliographic record
Abstract
The divergence of Artificial Intelligence (AI) with Next-Generation (xG) mobile networks is inevitable as it is driven by the demand for more intelligent mobile networks that can optimize the data collected from users’ devices and utilize the distributed nature of Federated Learning (FL). This poses a multitude of security challenges, including authentication, data integrity, and secure communication channels between participating network nodes. Traditional deployments of FL have not proven resilience against a range of attacks, like port scanning, man-in-the-middle, and network mapping. This paper proposes the SDP-FedStellar framework as a possible solution, aiming to address the security gap at the intersection of xG and FL. We establish a zero-trust security model using SDP’s dynamic controller-based authentication and authorization to ensure the privacy of user and model data privacy throughout the federated learning process, to enhance the overall security of xG networks running centralized or decentralized FL. This framework strengthens the network and each node’s ability to dynamically defend itself against attackers targeting malicious nodes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".