Hierarchical Multi-Modal Federated Learning Over Cell-Free Massive MIMO Systems

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.