Heterogeneous federated learning: State-of-the-art and research challenges

M Ye, X Fang, B Du, PC Yuen, D Tao - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Security and privacy on 6g network edge: A survey

B Mao, J Liu, Y Wu, N Kato - IEEE communications surveys & …, 2023 - ieeexplore.ieee.org
To meet the stringent service requirements of 6G applications such as immersive cloud
eXtended Reality (XR), holographic communication, and digital twin, there is no doubt that …

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 …

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 …

Exploiting shared representations for personalized federated learning

L Collins, H Hassani, A Mokhtari… - … on machine learning, 2021 - proceedings.mlr.press
Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …

Ditto: Fair and robust federated learning through personalization

T Li, S Hu, A Beirami, V Smith - International conference on …, 2021 - proceedings.mlr.press
Fairness and robustness are two important concerns for federated learning systems. In this
work, we identify that robustness to data and model poisoning attacks and fairness …

Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

A Fallah, A Mokhtari… - Advances in neural …, 2020 - proceedings.neurips.cc
Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

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 …

Federated learning: A survey on enabling technologies, protocols, and applications

M Aledhari, R Razzak, RM Parizi, F Saeed - IEEE Access, 2020 - ieeexplore.ieee.org
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Personalized cross-silo federated learning on non-iid data

Y Huang, L Chu, Z Zhou, L Wang, J Liu, J Pei… - Proceedings of the …, 2021 - ojs.aaai.org
Non-IID data present a tough challenge for federated learning. In this paper, we explore a
novel idea of facilitating pairwise collaborations between clients with similar data. We …