Energy and Distribution-Aware Cooperative Clustering Algorithm in Internet of Things (IoT)-Based Federated Learning
Jaewook Lee, Haneul Ko
Abstract
In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from the base station (BS), they consume lots of energy to transmit the updated parameters to BS, which can cause energy depletion of IoT devices. To mitigate this problem, IoT devices can be clustered and the clustered IoT devices transmit the updated parameters to their cluster headers (CHs) instead of BS. However, if the aggregated data distribution in each cluster is non-independent and identically distributed (non-IID), the desired accuracy of the model cannot be achieved. In this paper, we propose an energy and distribution-aware cooperative clustering algorithm (EDA-CCA) where several IoT devices with a sufficient energy level are selected as CHs. By considering the distance to these CHs and BS and the data distribution of IoT devices, other IoT devices are clustered, and then a hierarchical parameter aggregation (i.e., sequential aggregation within each cluster and synchronous aggregation between CHs and FL server) is conducted. Evaluation results demonstrate that EDA-CCA can make the model having the desired accuracy with the lowest energy consumption among the comparison schemes.