Federated Intelligence for Active Queue Management in Inter-Domain Congestion
Bibliographic record
Abstract
Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization's domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".