CE-Fed: Communication efficient multi-party computation enabled federated learning

CE-Fed: Communication efficient multi-party computation enabled federated learning

Federated learning (FL) allows a number of parties collectively train models without revealing private datasets. There is a possibility of extracting personal or confidential data from the shared models even-though sharing of raw data is prevented by federated learning. Secure Multi Party Computatio...

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Bibliographic Details
Main Authors: Goh, R.S.M (Author), Kanagavelu, R. (Author), Li, Z. (Author), Samsudin, J. (Author), Wang, S. (Author), Wei, Q. (Author), Yang, Y. (Author), Zhang, H. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Aggregates
Committee selection
Communication cost
Computational efficiency
Confidential data
Edge computing
Federated learning
Learning systems
Multiparty computation
Multi-party computation
Privacy preserving
Privacy-preserving techniques
Scalability
Secure multi-party computation
Shared model
Train model
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