Leveraging Fully-decoupled Radio Access Network for Wireless Federated Learning Acceleration
Bibliographic record
Abstract
Federated learning (FL) has emerged as an innovative machine learning (ML) paradigm that can utilize the computation capability of both the cloud and the end-users to support data-intensive tasks in the next-generation mobile communication networks. However, the collaboration between the cloud and the end-users is usually constrained by the worst wireless link quality during the uplink and downlink transmission. In this paper, targeting at reducing the FL training latency over the wireless networks, we leverage the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture through the coordinated multiple base stations (BSs) access mechanism. First, based on a proven data rate lower bound, we exploit the integer variable relaxation and the successive convex approximation (SCA) algorithms to transform the original computationally prohibitive non-convex problem into a solvable convex form. Subsequently, the Lagrange dual decomposition method is used to obtain an optimal BS serving cluster (OSC) for the uplink and downlink transmission respectively. Extensive simulations are conducted to verify the effectiveness of the proposed coordinated multiple BSs access solution for realizing a faster FL training task.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.043 | 0.041 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".