Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges

ETM Beltrán, MQ Pérez, PMS Sánchez… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

S Rani, A Kataria, S Kumar, P Tiwari - Knowledge-based systems, 2023 - Elsevier
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Edge artificial intelligence for 6G: Vision, enabling technologies, and applications

KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Fine-tuning global model via data-free knowledge distillation for non-iid federated learning

L Zhang, L Shen, L Ding, D Tao… - Proceedings of the …, 2022 - openaccess.thecvf.com
Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning on non-IID data: A survey

H Zhu, J Xu, S Liu, Y Jin - Neurocomputing, 2021 - Elsevier
Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Model-contrastive federated learning

Q Li, B He, D Song - … of the IEEE/CVF conference on …, 2021 - openaccess.thecvf.com
Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …

Federated learning on non-iid data silos: An experimental study

Q Li, Y Diao, Q Chen, B He - 2022 IEEE 38th international …, 2022 - ieeexplore.ieee.org
Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …

Fedproto: Federated prototype learning across heterogeneous clients

Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang… - Proceedings of the …, 2022 - ojs.aaai.org
Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

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 …

No fear of heterogeneity: Classifier calibration for federated learning with non-iid data

M Luo, F Chen, D Hu, Y Zhang… - Advances in Neural …, 2021 - proceedings.neurips.cc
A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …