Federated Self-Supervised Learning Based on Prototypes Clustering Contrastive Learning for Internet of Vehicles Applications
Bibliographic record
Abstract
Federated learning (FL) is a novel paradigm for distribute edge intelligence for the Internet-of-Vehicles (IoV) application, which can enable superior performance in model training without the need to share local data. However, in the actual architecture of FL, the existence of nonindependent and identically distributed (non-IID) data at the edge device, along with the involvement of randomly participating distributed nodes, can result in model bias and a subsequent decrease in overall performance. To solve this problem, a new federated self-supervised learning method based on prototypes clustering contrastive learning (FedPCC) is proposed, which can effectively addresses the issue of asynchronous edge training and global model bias by introducing an unsupervised prototypes layer. The prototypes layer maps edge features to a global space and performs clustering, facilitating the new aggregation method of global prototypes on the server. Then, models from other components are aggregated based on data weight. Besides that, during the parameter deployment phase, we replace the prototype layer to acquire global knowledge, while employing momentum updates to preserve the local knowledge of the other components. Finally, to assess the efficacy of our proposed approach, we carried out comprehensive experiments across the various data sets. The findings show that our method gains state-of-the-art performance, which also validates its effectiveness.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".