Related papers: Energy-Efficient Federated Edge Learning For Small…

Federated Learning(FL) is a privacy-preserving machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. These systems are often comprised of millions of user devices and only a…

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to…

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their…

As privacy protection gains increasing importance, more models are being trained on edge devices and subsequently merged into the central server through Federated Learning (FL). However, current research overlooks the impact of network…

Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the…

Internet of Things (IoT) devices and applications are generating and communicating vast quantities of data, and the rate of data collection is increasing rapidly. These high communication volumes are challenging for energy-constrained,…

Federated learning is used for decentralized training of machine learning models on a large number (millions) of edge mobile devices. It is challenging because mobile devices often have limited communication bandwidth and local computation…

Edge AI, which brings artificial intelligence to the edge of the network for real-time processing and decision-making, has emerged as a transformative technology across various applications. However, the deployment of Edge AI systems faces…

In Federated Learning (FL), with parameter aggregated by a central node, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the single point of failure within the FL framework, recent studies…

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing…

As a promising solution to achieve efficient learning among isolated data owners and solve data privacy issues, federated learning is receiving wide attention. Using the edge server as an intermediary can effectively collect sensor data,…

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or…

Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI) models while maintaining data privacy. To overcome the…

Federated learning is a distributed machine learning approach in which clients train models locally with their own data and upload them to a server so that their trained results are shared between them without uploading raw data to the…

In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and…

This research introduces a revolutionary paradigm for HetNet management, presenting an innovative algorithmic framework that transcends traditional notions of network capacity enhancement. Our exploration delves into the intricate interplay…

The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and…

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse…

In the age of technology, data is an increasingly important resource. This importance is growing in the field of Artificial Intelligence (AI), where sub fields such as Machine Learning (ML) need more and more data to achieve better results.…

Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL…