4 research outputs found
Federated Generative Learning with Foundation Models
Existing federated learning solutions focus on transmitting features,
parameters or gadients between clients and server, which suffer from serious
low-efficiency and privacy-leakage problems. Thanks to the emerging foundation
generative models, we propose a novel federated learning framework, namely
Federated Generative Learning, that transmits prompts associated with
distributed training data between clients and server. The informative training
data can be synthesized remotely based on received prompts containing little
privacy and the foundation generative models. The new framework possesses
multiple advantages, including improved communication efficiency, better
resilience to distribution shift, substantial performance gains, and enhanced
privacy protection, which are verified in extensive experiments on ImageNet and
DomainNet datasets
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
As an emerging technique, Federated Learning (FL) can jointly train a global
model with the data remaining locally, which effectively solves the problem of
data privacy protection through the encryption mechanism. The clients train
their local model, and the server aggregates models until convergence. In this
process, the server uses an incentive mechanism to encourage clients to
contribute high-quality and large-volume data to improve the global model.
Although some works have applied FL to the Internet of Things (IoT), medicine,
manufacturing, etc., the application of FL is still in its infancy, and many
related issues need to be solved. Improving the quality of FL models is one of
the current research hotspots and challenging tasks. This paper systematically
reviews and objectively analyzes the approaches to improving the quality of FL
models. We are also interested in the research and application trends of FL and
the effect comparison between FL and non-FL because the practitioners usually
worry that achieving privacy protection needs compromising learning quality. We
use a systematic review method to analyze 147 latest articles related to FL.
This review provides useful information and insights to both academia and
practitioners from the industry. We investigate research questions about
academic research and industrial application trends of FL, essential factors
affecting the quality of FL models, and compare FL and non-FL algorithms in
terms of learning quality. Based on our review's conclusion, we give some
suggestions for improving the FL model quality. Finally, we propose an FL
application framework for practitioners
A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions
Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies