In our second post we described attacks on models and the concepts of input privacy and output privacy . ln our last post , we described horizontal and vertical partitioning of data in privacy-preserving federated learning (PPFL) systems. In this post, we explore the problem of providing input privacy in PPFL systems for the horizontally-partitioned setting. Models, training, and aggregation To explore techniques for input privacy in PPFL, we first have to be more precise about the training process. In horizontally-partitioned federated learning, a common approach is to ask each participant to
Related Posts
EXECUTIVE SUMMARY: The powerful next-generation artificial intelligence-based tool known as…
- rooter
- March 16, 2023
- 4 min read
- 0
By Carolyn Duby, Chief Technology Officer, Cloudera Government Solutions The…
- rooter
- October 15, 2023
- 1 min read
- 0
Hackers have evolved into snitches leveraging recent SEC legislation which…
- rooter
- November 21, 2023
- 4 min read
- 0
For years, cyber deception has been an excellent tool against…
- rooter
- January 19, 2025
- 1 min read
- 0