Hierarchical Multimodal Federated Learning Over Cell-Free Massive MIMO Systems
Bibliographic record
Abstract
Cell-Free massive MIMO (CF-mMIMO) is a promising technology for enabling Federated Learning (FL) in the next generation of wireless networks due to its uniform service coverage. However, existing approaches that optimize FL over CF-mMIMO networks rely on a single Control Unit (CU), limiting scalability in terms of geographic coverage and user participation, while also overlooking multi-modal data heterogeneity, which further increases latency. To address these challenges, we propose Hierarchical Multimodal Federated Learning (HMFL) over CF-mMIMO networks, which employs multiple CUs, managed by a Cloud Data Center (CDC). Instead of a single CU for global aggregation, HMFL uses a hierarchical approach where each CU aggregates local updates from the users before forwarding the edge models to the CDC for global aggregation. Moreover, we formulate an optimization problem for long-term decision-making in HMFL over CF-mMIMO networks, aiming to balance latency and user participation under a long-term energy budget. To solve this problem, we propose Long-Term Device-Modality Selection and Resource Allocation (LT-DeMoSRA) that employs optimization techniques to enable per-round decision-making with a long-term perspective over CUs without requiring future information. Additionally, our HMFL framework personalizes the fusion process based on the available modalities for each user, ensuring more adaptive and efficient multi-modal learning. Experimental results demonstrate that HMFL over multi-CU CF-mMIMO networks supports a larger number of users and outperforms existing alternatives by reducing training latency and improving user participation for both unimodal and multimodal data.
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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 | 0.002 |
| 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".