Cooperative Detection Algorithm of Malicious Nodes Based on Federated Learning
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
In Mobile Crowdsensing (MCS) networks, traditional malicious user detection methods typically rely on transmitting vast amounts of raw data to a central server for analysis. This approach not only incurs significant communication overhead, exacerbating network congestion, but also poses a high risk of exposing users' sensitive data. To address these challenges, this paper introduces an MCS malicious behavior detection framework that integrates the concepts of Federated Learning (FL) and edge computing. This framework employs a distributed architecture centered around edge servers, enabling multiple edge nodes to process data locally and collaboratively train detection models, thereby effectively safeguarding user privacy. Additionally, to counter potential malicious users in federated learning, a legitimate user identification method based on user contribution levels is designed using the gradient similarity principle. By excluding malicious users, the system can mitigate the risk of attacks, ultimately enhancing the accuracy and security of the system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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