PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognition.

Federated Learning (FL) has emerged as a promising approach to addressing issues related to centralized machine learning such as data privacy, security and access. Nevertheless, it also brings new challenges incurred by heterogeneity among data statistical levels, devices and models in the context of multi-level federated learning (MlFed) architecture. In this paper, we conceive a new Personalized Multi-level Federated Learning (PerMl-Fed) framework, which extends existing MlFed architecture with three specialized personalized FL methods to address the three challenges. Specially, we design a Transfer Multi-level Federated Learning (TrMlFed) model to mitigate statistical heterogeneity across multiple layers of FL. We propose an Asynchronous Multi-level Federated Learning (AsMlFed) approach which allows asynchronous update in MlFed, thus alleviating device heterogeneity. We develop a Deep Mutual Multi-level Federated Learning (DmMlFed) method based on the concept of deep mutual learning to tackle model heterogeneity. We evaluate the PerMl-Fed framework and associated technologies on the public Wireless Sensor Data Mining (WISDM) dataset. Initial results demonstrate improved average accuracy of 7 % and achieves accuracy ranging from 84 to 92 % across eight different hierarchical group structures. [ABSTRACT FROM AUTHOR]