Multi-Modal Federated Learning Over Cell-Free Massive MIMO Systems for Activity Recognition
Bibliographic record
Abstract
This paper addresses the problem of Multi-modal Federated Learning (MFL) over resource-limited Cell-Free massive MIMO (CF-mMIMO) networks for the application of Human Activity Recognition (HAR). MFL leverages diverse data modalities across various clients, while the CF-mMIMO network ensures consistent service quality, crucial for collaborative training. The primary challenges of MFL are data heterogeneity, which includes statistical and modality heterogeneity that complicate data fusion, client collaboration, and inference with missing data, and system heterogeneity, where devices with dissimilar modalities experience varied processing and communication delays, increasing overall training latency. To tackle these issues, we propose a late-fusion model architecture that allows flexible client participation with any combination of data modalities, and formulate an optimization problem to jointly minimize latency and global loss in MFL. We propose a prioritized device-modality selection scheme that allows flexible participation of devices. Additionally, we employ a modified Particle Swarm Optimization (PSO) algorithm for efficient resource allocation. Extensive experiments validate our framework, demonstrating substantial reductions in training time and significant improvements in model performance, particularly an average improvement of 15% and 23% in test accuracy compared to the other fusion models when missing one and two modalities in the inference phase.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".