Non-IID Degree Aware Adaptive Federated Learning Procedure Selection Scheme for Edge-Enabled IoT Network

Due to the independent, identically distributed (non-IID) nature of IoT device data, the traditional federated learning (FL) procedure, where IoT devices train the deep model in parallel, suffers from a degradation in learning accuracy. To mitigate this problem, a sequential FL procedure has been proposed, in which IoT devices train the deep model in a serialized manner via a parameter server. However, this approach experiences a longer convergence time due to the lack of parallelism. In this paper, we propose an adaptive FL procedure selection (AFLS) scheme that selects an appropriate FL scheme, either the traditional or the sequential FL procedures, based on the degree of non-IID among IoT devices to achieve both the required learning accuracy and low convergence time. To further reduce the convergence time of the sequential FL procedure, we also introduce a device-to-device (D2D)-based sequential FL procedure. The evaluation results demonstrate that AFLS can reduce convergence time by up to 16% compared to the sequential FL procedure and improve learning accuracy by up to 6∼26% compared to the traditional FL procedure.