Cost-efficient Federated Reinforcement Learning- Based Network Routing for Wireless Networks
Bibliographic record
Abstract
Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing problems. However, the lack of up-to-date training data, slow convergence, and low robustness due to the dynamic change of the network topology, makes these AI-based routing systems inefficient. To address this problem, Reinforcement Learning (RL) has been introduced to design more flexible and robust network routing protocols. However, the amount of data ($i$. e., state-action space) shared be- tween agents, in a Multi-Agent Reinforcement Learning (MARL) setup, can consume network bandwidth and may slow down the process of training. Moreover, the curse of dimensionality of RL encompasses the exponential growth of the discrete state-action space, thus limiting its potential benefit. In this paper, we present a novel approach combining Federated Learning (FL) with Deep Reinforcement Learning (D RL) in order to ensure an effective network routing in wireless environment. First, we formalize the problem of network routing as a problem of RL, where multiple agents that are geographically distributed train the policy model in a fully distributed manner. Thus, each agent can quickly obtain the optimal policy that maximizes the cumulative expected reward, while preserving the privacy of each agent's data. Experiments results show that our proposed Federated Reinforcement Learning (FRL) approach is robust and effective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".