Towards personalized federated learning
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …
research, there has been growing awareness and concerns of data privacy. Recent …
Model optimization techniques in personalized federated learning: A survey
Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
Federated learning and its role in the privacy preservation of IoT devices
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized
problem-solving technique that allows users to train using massive data. Unprocessed …
problem-solving technique that allows users to train using massive data. Unprocessed …
Three approaches for personalization with applications to federated learning
The standard objective in machine learning is to train a single model for all users. However,
in many learning scenarios, such as cloud computing and federated learning, it is possible …
in many learning scenarios, such as cloud computing and federated learning, it is possible …
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
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 …
learning with non-IID data. To cope with this, most of the existing works involve enforcing …
Layer-wised model aggregation for personalized federated learning
Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …
from broad range of distributed data, but also support customized models for heterogeneous …
On bridging generic and personalized federated learning for image classification
Federated learning is promising for its capability to collaboratively train models with multiple
clients without accessing their data, but vulnerable when clients' data distributions diverge …
clients without accessing their data, but vulnerable when clients' data distributions diverge …
Federated reinforcement learning with environment heterogeneity
Abstract We study Federated Reinforcement Learning (FedRL) problem in which $ n $
agents collaboratively learn a single policy without sharing the trajectories they collected …
agents collaboratively learn a single policy without sharing the trajectories they collected …
Dynamic personalized federated learning with adaptive differential privacy
Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …
solution to address non-IID distribution of data and privacy leakage risks. However, current …
A survey on heterogeneous federated learning
Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …
the isolated data silos by cooperatively training models among organizations without …