Cross-Device Distributed Federated Learning Coalition Formation Game for Constrained IoT
Stephane Durand, Kinda Khawam, Dominique Quadri, Samer Lahoud, Steven Martin · IEEE Internet of Things Journal · 2025
Edge computing is an efficient way to help constrained IoT devices by offloading heavy tasks on edge servers, especially computing tasks related to Machine Learning (ML). Moreover, such devices can only store a limited amount of data because of their reduced capacity. Consequently, ML is bound to be smeared with relatively high error prediction as these devices resort to a small training dataset for their learning. To mend that issue, IoT devices can group in clusters and resort to Federated Learning (FL) with their pairs in the same cluster or coalition. However, the learned model needs to be transmitted repeatedly over a wireless access network, which is energy consuming. Hence, although learning collectively through FL can reduce the learned model variance, it inflicts a communication cost, dependent on the coalition size, that must be taken into account. Therefore, a cost function is devised astutely by factoring in both the prediction error and communication cost in a learning cluster. Then, a coalition formation game is conceived to minimize the devised cost function. Autonomous IoT devices will engage in the proposed game leading to coalitions of optimal size. Once clusters are formed, distributed FL is applied in any cluster in order to reduce the learning error of participating devices while curbing their communication cost.