A Federated Convolution Transformer for Fake News Detection
Bibliographic record
Abstract
We present a novel approach to detecting fake news in Internet of Things (IoT) applications. By investigating federated learning and trusted authority methods, we address the issue of data security during training. Simultaneously, by investigating convolution transformers and user clustering, we deal with multi-modality in fake news data. Firstly, we use dense embedding and the k-means algorithm to cluster users into groups that are similar to one another. We then develop a local model for each user using their local data. The server then receives the local models of the users along with the clustering information, and a trusted authority verifies their integrity there. We use two different types of aggregation in place of conventional federated learning systems. The initial step is to combine all the users' models to create a single global model. The second step entails compiling each user's model into a local model of comparable users. Both models are supplied to the users, who then select the most suitable model for identifying fake news. By conducting extensive experiments using Twitter data, we demonstrate that the proposed method outperforms various baselines, where it achieves an average accuracy of 0.85 in comparison to others that do not exceed 0.81.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".