A Continuous Authentication Approach for Mobile Crowdsourcing Based on Federated Learning
Bibliographic record
Abstract
With the widespread use of smartphones and wearable devices, Mobile Crowdsourcing (MCS) has become a powerful method for gathering and processing data from various users. MCS offers several advantages, including improved mobility, scalability, cost-effectiveness, and the utilization of collective human intelligence. However, ensuring the authenticity of users throughout the data collection process remains a challenge. Current authentication methods, such as traditional PIN codes, two-factor authentication, and biometric authentication, often struggle to provide continuous verification while adequately protecting user privacy. This paper addresses this issue by proposing a new continuous authentication approach based on Federated Learning. This approach combines continuous identity verification with privacy preservation benefits, allowing for the ongoing validation of user authenticity during data collection while improving authentication accuracy. We also discuss the non-Independently and Identically Distributed issue in Federated Learning and employ transfer learning techniques based on feature extraction to enhance the performance of the authentication models. We conducted extensive experiments using various datasets to evaluate the effectiveness of our proposed method. The results of this study demonstrate its potential to enhance the security and privacy of MCS systems.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".