A Model and Infrastructure for Federated Learning Content Repositories

By D. Rehak, P. Dodds, L. Lannom. 2005

48 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Raymond Cheng
2. Nicholas Hynes
3. Dawn Song
4. Florian Tramèr
5. Donggang Liu
6. Brent Waters
7. Adrian Perrig
8. Dan Boneh

Citations in the corpus

• LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018, background): “[1] established CORDRA, a framework that provided standards for an interoperable repository infrastructure where data repositories were clustered into community federations and their data were retrieved by a global federation using the”
• LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data (2018, neutral): “− N ∑ i=1 [yi log f (xi)+(1− yi) log(1− f (xi))] (1)”
• Efficient Deep Learning on Multi-Source Private Data (2018, background): “For instance, federated learning [26], designed to allow multiple users to jointly train a private model, has been shown to be vulnerable against certain privacy attacks [17].”