FedStream: Prototype-Based Federated Learning on Distributed Concept-Drifting Data Streams
Why this work is in the frame
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Bibliographic record
Abstract
Distributed data stream mining has gained increasing attention in recent years since many organizations collect tremendous amounts of streaming data from different locations. Existing studies mainly focus on learning evolving concepts on distributed data streams, while the privacy issue is little investigated. In this article, for the first time, we develop a federated learning framework for distributed concept-drifting data streams, called FedStream. The proposed method allows capturing the evolving concepts by dynamically maintaining a set of prototypes with error-driven representative learning. Meanwhile, a new metric-learning-based prototype transformation technique is introduced to preserve privacy among participating clients in the distributed data streams setting. Extensive experiments on both real-world and synthetic datasets have demonstrated the superiority of FedStream, and it even achieves competitive performance with state-of-the-art distributed learning methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it