Secure AirComp Federated Learning With STAR–IRS
Bibliographic record
Abstract
In wireless environments, over-the-air computation (AirComp) can exploit the superposition nature of the channel to facilitate federated learning (FL). When a large number of Internet of Things (IoT) devices participate in the FL process, employing a simultaneously transmitting and reflecting (STAR) intelligent reflecting surface (IRS) can significantly improve FL aggregation performance by enabling wider user participation. However, the STAR–IRS may also introduce substantial security risks as eavesdroppers can also enjoy the benefits of STAR–IRS. In this article, a secure design that enhances the mean-squared error (MSE) performance of the basestation (BS) while disrupting the eavesdropper’s performance is proposed. The BS transmits a jamming noise to the eavesdropper on the same frequency band that devices transmit their local models to the BS. An optimization problem is formulated to minimize the aggregation error at the BS while ensuring the error exceeds a certain threshold at the eavesdropper. Then, a multistep procedure, utilizing alternating optimization (AO) and difference-of-convex (DC) algorithm, is proposed to solve this problem. Numerical results demonstrate the effectiveness of the proposed solution, resulting in low MSE and fast convergence in the learning process for the BS, while also deteriorating the eavesdropper’s learning and detection performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".