xG Security: Zero-Trust and Moving Target Defense in Decentralized Learning Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it