Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers
Bibliographic record
Abstract
Recently, Federated Learning (FL) has been applied in various research domains specially because of its privacy preserving and decentralized approach of model training. However, very few FL applications have been developed for the Radio Access Network (RAN) due to the lack of efficient deployment models. Open RAN (O-RAN) promises a high standard of meeting 5G services through its disaggregated, hierarchical, and distributed network function processing framework. Moreover, it comes with built-in intelligent controllers to instill smart decision making ability into RAN. In this paper, we propose a framework named O-RANFed to deploy and optimize FL tasks in O-RAN to provide 5G slicing services. To improve the performance of FL we formulate a joint mathematical optimization model of local learners selection and resource allocation to perform model training in every iteration. We solve this non-convex problem using the decomposition method. First, we propose a slicing based and deadline aware client selection algorithm. Then, we solve the reduced resource allocation problem by using successive convex approximation (SCA) method. Our simulation results show the proposed model outperforms the state-of-the-art FL methods such as FedAvg and FedProx in terms of convergence, learning time, and resource costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".