Large-Scale Federated Learning for Hybrid Cell-Free Massive MIMO and Visible Light Communication Systems
Bibliographic record
Abstract
Next-generation wireless networks demand scalable and low-latency distributed learning frameworks that can operate across diverse communication environments. This paper proposes CFVLC, a novel large-scale Federated Learning (FL) framework over a resource-constrained wireless network, involving outdoor users connected through Cell-Free massive MIMO (CF-mMIMO) system, and indoor users distributed across multiple environments supported by Visible Light Communication (VLC) technology. To address the extreme system heterogeneity from different environments, CFVLC introduces an optimization problem that jointly performs device selection and resource allocation to minimize training latency across the network. We propose a two-stage heuristic solution that intelligently balances user participation across environments with varying resources and channel conditions. Extensive simulations demonstrate that our approach significantly improves training latency and model performance compared to conventional schemes, highlighting the benefits of integrating outdoor CF-mMIMO and indoor VLC technologies for large-scale FL.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".