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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 +5 more
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Privatized Graph Federated Learning
EURASIP Journal on Advances in Signal Processing, 2023 Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
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Federated learning in food research
Journal of Agriculture and Food ResearchThe 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 +5 more
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Hierarchical Federated Learning Algorithm Based on EMD Optimal Matching [PDF]
Jisuanji gongchengFederated learning allows multiple clients to cooperatively train a high-performance global model without sharing private data. In a horizontal federated learning environment involving cross-silo scenarios, the statistical heterogeneity in the ...
WU Xiaohong, LI Pei, GU Yonggen, TAO Jie
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Iraqi Journal for Computer Science and Mathematics, 2023 This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep learning and CNN architecture ...
Alaa Hamza Omran +2 more
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2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020 Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need ...
Zhikun Chen +4 more
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Blockchain-Based Federated Learning: A Survey and New Perspectives
Applied SciencesFederated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy.
Weiguang Ning +9 more
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Survey of Federated Incremental Learning [PDF]
Jisuanji kexueFederated 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
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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 +3 more
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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 +3 more
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