Toward Power-Aware Federated Activity Detection in Cell-Free Massive MIMO-Based mMTC Networks
The rapid growth of massive machine-type communications (mMTC), combined with advances in edge intelligence, is paving the way for low-latency, low-overhead connectivity. However, the sporadic nature of device activity in mMTC scenarios calls for efficient methods to determine which devices are active at any given time. This motivates collaborative learning within a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture, where the wide geographical distribution of access points (APs) and their joint coordination make distributed learning efficient and secure. Consequently, federated learning (FL) emerges as a promising solution. Indeed, FL enables participants to train a shared model without exchanging raw local data, thereby enhancing data privacy at the AP side and lowering the fronthaul load while leveraging heterogeneous, location-dependent data. The present study proposes a novel FL framework where the CF-mMIMO participants are APs. Due to differences in device behavior, mobility patterns, and environmental factors across the network, the data collected at each AP is often non-independent and non-identically distributed (non-IID). This heterogeneity slows down the convergence of standard FL training and increase variability among client updates, particularly under heterogeneous radio feature distributions. To address this, we propose a client selection strategy that prioritizes APs based on their average received signal power. Our approach shows competitive performance compared to baseline methods, while also addressing the scalability and privacy requirements of mMTC systems. Furthermore, our study analyzes the fairness achieved by APs across devices and presents a representative, percentage-scale analysis of power-consumption gains relative to detection performance when some APs are dropped (i.e., taken out of service), examining two AP-dropping strategies. These results bring valuable insights and set guidelines towards the implementation of FL-based activity detection in CF-mMIMO networks.