Add to ORKGFederated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security. However, FL faces heterogeneity from various aspects, including data space, statistical, and system heterogeneity. For example, collaborative organizations without conflict of interest often come from different areas and have heterogeneous data from different feature spaces. Participants may also want to train heterogeneous personalized local models due to non-IID and imbalanced data distribution and various resource-constrained devices. Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL. In this survey,...
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a pa...
A possible approach to address the increasing security and privacy concerns is federated learning (F...
In recent years, more and more attention has been paid to the privacy issues associated with storing...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Machine learning models benefit from large and diverse training datasets. However, it is difficult f...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
The next era of privacy preserving machine learning is built upon the basic principle centered aroun...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a pa...
A possible approach to address the increasing security and privacy concerns is federated learning (F...
In recent years, more and more attention has been paid to the privacy issues associated with storing...
Though successful, federated learning presents new challenges for machine learning, especially when ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Machine learning models benefit from large and diverse training datasets. However, it is difficult f...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated Learning (FL) has been an area of active research in recent years. There have been numerou...
The next era of privacy preserving machine learning is built upon the basic principle centered aroun...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a pa...
A possible approach to address the increasing security and privacy concerns is federated learning (F...
In recent years, more and more attention has been paid to the privacy issues associated with storing...