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Jan 4, 2023 · This renders existing secure aggregation protocols resource-intensive, especially for urban sensing applications, having a spatiotemporal model ...
This paper presents resource adaptive (ReAd) Turbo-Aggregate, a secure multi-party aggregation protocol for dynamic spatiotemporal applications.
Federated learning has been proposed as a privacy-preserving alternative to conventional cloud-based systems dealing with sensitive and private user data.
This approach scales efficiently across multiple servers and enhances privacy without compromising computational efficiency. Gupta et al. [22] targeted the ...
Bibliographic details on A Resource Adaptive Secure Aggregation Protocol for Federated Learning based Urban Sensing Systems.
A Resource Adaptive Secure Aggregation Protocol for Federated Learning based Urban Sensing Systems. S Gupta, A Kapoor, D Kumar. Proceedings of the 6th Joint ...
A Resource Adaptive Secure Aggregation Protocol for Federated Learning based Urban Sensing Systems. COMAD/CODS 2023: 135. [+][–]. Coauthor network. maximize.
A Resource Adaptive Secure Aggregation Protocol for Federated Learning based Urban Sensing Systems. S Gupta, A Kapoor, D Kumar. Proceedings of the 6th Joint ...
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A Resource Adaptive Secure Aggregation Protocol for Federated Learning based Urban Sensing Systems. 2023-01-04 | Conference paper.
Jun 24, 2023 · This paper presents an optimised Turbo-Aggregate protocol, we call Resource Adaptive (ReAd) Turbo-Aggregate, which is a secure multiparty computation scheme.