# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
### Related Work  
While Air-FL offers notable substantial benefits, its deployment in multi-cell wireless networks introduces significant challenges [14]. In particular, concurrent FL tasks among multiple cells can cause severe inter-cell interference, which undermines the quality of aggregated updates and slows down model convergence [15; 16]. This issue is especially acute in parallel multi-task scenarios, where each cell tackles different learning tasks with heterogeneous data and distinct model objectives [17; 18].  
Existing strategies to tackle interference in multi-cell Air-FL primarily involve centralized optimization [19; 20]. For example, the authors in [19; 20] characterized the Pareto boundary of the error-induced gap region to quantify learning performance trade-offs among different FL tasks, which formulates optimization problems to minimize the sum of error-induced gaps across all cells. Although such centralized solutions achieve near-optimal performance, they suffer from scalability and latency issues in large or dynamic networks, due to the dependence on global channel state information (CSI) and centralized coordination.  
To address these concerns, other works have introduced innovations such as hierarchical personalized FL frameworks with optimized beamforming [21], and dynamic clustering combined with power control for two-tier FL systems [22]. Additionally, methods based on compressed sensing [23] have been proposed to enable cross-cell model aggregation with privacy preservation and a reduced need for strict synchronization. The authors in [24] investigated a theoretically guaranteed two-stage search algorithm for joint edge association and aggregation optimization, extended with flexible bandwidth allocation. However, these methods still require some inter-cell coordination.  
Despite these advances, centralized methods still face limitations in real-world deployments due to the signaling overhead and latency. This paper addresses that gap by proposing a distributed power control framework guided by interference temperature (IT) parameters, offering near-Pareto optimal performance without requiring centralized coordination. Compared with existing centralized interference management strategies, such as joint power control and beamforming [19; 20], our approach introduces several key advancements. First, prior works typically rely on centralized optimization frameworks that require global CSI and full inter-cell coordination, which pose challenges in terms of scalability, latency, and signaling burden. In contrast, our proposed scheme adopts a distributed optimization perspective based on IT, which enables each cell to manage its transmit power using only local CSI and lightweight inter-cell communication. Second, while [19; 20] focus on minimizing global error-induced gaps via Pareto boundary characterization under centralized control, our framework introduces a novel dynamic IT update mechanism that allows the system to iteratively approximate Pareto optimality in a decentralized manner. These features make our approach more suitable for large-scale, heterogeneous, and dynamic wireless learning scenarios. A comparison of the focuses, contributions, and potential limitations among highly relevant studies is provided in Table 1.