Lightweight Authentication in Edge Collaborations Utilizing Multi-dimensional Historical Information: Design and Implementation
Bibliographic record
Abstract
While edge collaborations play more and more important roles in the sixth-generation (6G) network, the authentication among devices for trusted collaborations is more challenging. The existing authentication mechanisms may suffer from the long latency and high computation overhead in such application scenarios, especially when the collaboration group is large. In this paper, a lightweight group authentication scheme based on multi-dimensional historical information is proposed and implemented in a specific federated learning-based collaborative outdoor localization scenario. To further illustrate, the multi-dimensional historical information consists of the learning parameters and environment sensing data collected by lidar from the latest round of collaboration. Then, we design a key generation strategy based on the historical information and develop a group authentication protocol. In the proposed scheme, every device in the group can identify the others at once based on their broadcasting keys, which will be renewed automatically before every round of collaboration. Hence, the proposed scheme achieves lightweight group authentication and high security. Both implementation and simulation results demonstrate the validity and superior performance of the proposed scheme compared with the existing schemes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".