Results 41 to 50 of about 96,144 (271)

Active Federated Learning

CoRR, 2019
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is important.
Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, Anuj Kumar   +5 more
openaire   +2 more sources

Federated learning in food research

Journal of Agriculture and Food Research
The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine
Zuzanna Fendor, Bas H.M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali Hürriyetoğlu   +5 more
doaj   +1 more source

Survey of Federated Incremental Learning [PDF]

Jisuanji kexue
Federated learning,with its unique distributed training mode and secure aggregation mechanism,has become a research hotspot in recent years.However,in real-life scenarios,local model training often faces new data,leading to catastrophic forgetting of old
XIE Jiachen, LIU Bo, LIN Weiwei , ZHENG Jianwen
doaj   +1 more source

Blockchain-Based Federated Learning: A Survey and New Perspectives

Applied Sciences
Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy.
Weiguang Ning, Yingjuan Zhu, Caixia Song, Hongxia Li, Lihui Zhu, Jinbao Xie, Tianyu Chen, Tong Xu, Xi Xu, Jiwei Gao   +9 more
doaj   +1 more source

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

JMIR mHealth and uHealth, 2021
BackgroundThe use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health.
Liu, Jessica Chia, Goetz, Jack, Sen, Srijan, Tewari, Ambuj   +3 more
doaj   +1 more source

A Hybrid Approach to Privacy-Preserving Federated Learning

, 2019
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees ...
Anwar, Ali, Baracaldo, Nathalie, Ludwig, Heiko, Steinke, Thomas, Truex, Stacey, Zhang, Rui, Zhou, Yi   +6 more
core   +1 more source

Federated meta learning: a review

大数据, 2023
With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use ...
Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO   +3 more
doaj  

A Review of Federated Learning in Agriculture

Sensors, 2023
Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems.
Krista Rizman Zalik, Mitja Zalik
openaire   +5 more sources

Research review of federated learning algorithms

大数据, 2020
In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related
Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, Linjie CHEN, Yi LIU, Anxun HE, Jing XIAO   +6 more
doaj  

PeFLL: Personalized Federated Learning by Learning to Learn

, 2023
We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation ...
Scott, Jonathan A, Zakerinia, Hossein, Lampert, Christoph   +2 more
openaire   +4 more sources