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Mobility-Aware Federated Learning: Optimizing Performance With Interpretable Models and Wireless Channel Resource Allocation
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DOI：10.1109/TMC.2025.3624803.png)
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Abstract
En 中文
The integration of the Internet of Things (IoT) with Federated Learning (FL) offers a transformative approach to addressing the challenges of massive data processing and privacy preservation in distributed systems. As a decentralized machine learning paradigm, FL enables model training on distributed datasets while safeguarding data privacy, making it well-suited for IoT applications. However, the performance of wireless FL systems is often constrained by limited communication resources and the mobility of participating clients, which can disrupt efficient model training and convergence. In this paper, we propose a novel mobility-aware FL scheduling strategy that leverages interpretable machine learning to enhance resource allocation in wireless networks. A more effective and fair resource allocation strategy can be achieved by dynamically adjusting the weight of the model quality and the communication quality of the training participants. We evaluate the proposed strategy against traditional scheduling methods in both single and multi-base station scenarios. Simulation results reveal that our approach significantly enhances overall learning efficiency by prioritizing high-value local models. Furthermore, for mobile clients, we identify an optimal range of average speed and participant numbers that maximizes the performance of wireless FL systems, offering practical insights for real-world deployments.
Keywords:
Federated learning (FL)
interpretable machine learning (XAI)
Internet of Things (IoT)
wireless communications
client scheduling
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