Federated learning review: Fundamentals, enabling technologies, and future applications

S Banabilah, M Aloqaily, E Alsayed, N Malik… - Information processing & …, 2022 - Elsevier
Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

A survey on federated learning for resource-constrained IoT devices

A Imteaj, U Thakker, S Wang, J Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Federated learning for internet of things: Recent advances, taxonomy, and open challenges

LU Khan, W Saad, Z Han, E Hossain… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

[HTML][HTML] Federated learning in smart city sensing: Challenges and opportunities

JC Jiang, B Kantarci, S Oktug, T Soyata - Sensors, 2020 - mdpi.com
Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

Asynchronous online federated learning for edge devices with non-iid data

Y Chen, Y Ning, M Slawski… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Tifl: A tier-based federated learning system

Z Chai, A Ali, S Zawad, S Truex, A Anwar… - Proceedings of the 29th …, 2020 - dl.acm.org
Federated Learning (FL) enables learning a shared model acrossmany clients without
violating the privacy requirements. One of the key attributes in FL is the heterogeneity that …

Realizing the heterogeneity: A self-organized federated learning framework for IoT

J Pang, Y Huang, Z Xie, Q Han… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data.
Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air …

pfl-bench: A comprehensive benchmark for personalized federated learning

D Chen, D Gao, W Kuang, Y Li… - Advances in Neural …, 2022 - proceedings.neurips.cc
Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local
models, has gained increasing attention in recent years due to its success in handling the …

Ibm federated learning: an enterprise framework white paper v0. 1

H Ludwig, N Baracaldo, G Thomas, Y Zhou… - arXiv preprint arXiv …, 2020 - arxiv.org
Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

FedAT: A high-performance and communication-efficient federated learning system with asynchronous tiers

Z Chai, Y Chen, A Anwar, L Zhao, Y Cheng… - Proceedings of the …, 2021 - dl.acm.org
Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized and private. This form of collaborative learning exposes …