Performance comparison of randomized gossip, broadcast gossip and collection tree protocol for distributed averaging
Why this work is in the frame
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Bibliographic record
Abstract
Gossip and tree-based aggregation algorithms are two popular solutions for distributed averaging in wireless networks. The former uses only local message exchanges and requires no routing structures whereas the latter requires building a spanning tree. In this paper we provide a detailed comparison of their performance in terms of communication overhead, accuracy, latency and energy consumption using the network simulator Castalia. We use randomized gossip, broadcast gossip and the collection tree protocol as practical representatives in each category. Through simulations, we show that broadcast gossip requires, in general, the least communication overhead and lowest latency and energy at the expense of lower accuracy. Randomized gossip requires more transmissions than broadcast gossip, but has higher accuracy. The collection tree protocol requires, in general, the most communication overhead.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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