Retracted: Communication-Efficient Personalized Federated Meta-Learning in Edge Networks
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 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".