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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".