Federated Learning Empowered Routing for Opportunistic Network Environments
Bibliographic record
Abstract
Opportunistic network systems (OppNets), a subclass of Delay-tolerant networks (DTNs) systems, are a category of wireless networks designed for operation in demanding and constantly changing environments, where traditional network infrastructure is often unreliable, constrained, or absent. These adverse network conditions make conventional routing algorithms impractical. This paper proposes a Federated Learning-based Router (FLRouter) suitable for use in Opportunistic network environments, which harnesses the capabilities of federated learning. In this framework, the nodes opportunistically select the next-hop relay for message forwarding based on the real-time network state and nodes' local knowledge. The contribution of this work is the seamless integration of federated learning algorithms into opportunistic network systems, allowing for optimal routing decisions. Through simulations and in-depth analysis, the effectiveness of our approach in achieving efficient routing within OppNets is demonstrated.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".