… learning models for resource-constrained IoT devices? To this end, we first explore the existing
studies on FL, relative assumptions for distributed implementation using IoT devices, … IoT …

… scalability of modern IoT networks and growing data privacy concerns. Federated Learning
(FL) … can enable many intelligent IoT applications, by allowing for AI training at distributed IoT …

… They allow training machine learning models with decentralized data … by federated learning
concerning IoT malware detection and studies security issues inherent to this new learning …

… of federated learning for IoT. We also identify seven challenges that act as the key barriers to
enabling FL for IoT … to inspire new research at the intersection of federated learning and IoT. …

… machine learning, it has still privacy concerns. In this paper, first, we present the recent
advances of federated learning towards enabling federated learning-powered IoT applications. A …

… of privacy preserving federated learning for private IoT Analytics,… investigation of federated
learning on edge devices,” in … with a recent focus on federated learning for edge computing …

… We propose a novel federated learning framework for healthcare IoT devices, where we …
tation load on the IoT devices, and communication overhead between the IoT devices and the …

… growth of IoT connected devices and … devices, decentralized computations are very much
in need. In this context, we propose in this article a Federated Learning based scheme for IoT …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) … of
interconnected devices, acting as local learners. FL can be applied to power-constrained …

… edge compute devices, such as mobile phones and IoT devices, to learn a shared … of
federated learning when local data is non-IID. We first show that the accuracy of federated learning …