A Federated Unlearning-Based Secure Management Scheme to Enable Automation in Smart Consumer Electronics Facilitated by Digital Twin
Bibliographic record
Abstract
In consumer electronics, integrating the Internet of Things (IoT) and Artificial Intelligence (AI) has transformed everyday devices into smart, interconnected systems. However, this progress brings significant challenges in resource management, privacy, and security, particularly with the increasing reliance on data-centric technologies like Deep Learning (DL). The introduction of the Right to Be Forgotten (RBF) policy further complicates data management in DL models. This paper presents a new method for automating consumer electronic devices using Federated Learning (FL). This approach involves training devices with the help of a Digital Twin (DT) and securely storing data on a redactable blockchain after each training cycle. An unlearning mechanism in FL is adapted to meet RBF policy requirements, with the redactable blockchain facilitating the necessary data adjustments. Dual authentication methods are used to prevent malicious attacks: a hampel filter and performance checks during training, and a two-phase system comprising an XoR filter and continuous counter checks for request validation. A proof of concept confirms the system’s effectiveness, demonstrating its superior performance compared to existing methods.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".