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
This evening’s episode of Panorama on BBC One, Fighting Cyber…
- rooter
- July 21, 2025
- 5 min read
- 0
As an industry and a society, we are finally making…
- rooter
- August 30, 2024
- 1 min read
- 0
How Generative AI Strengthens Digital Fortresses By Dean Frankhauser, CEO,…
- rooter
- November 7, 2023
- 1 min read
- 0
The ransomware operation known as Rhysida has rapidly gained notoriety,…
- rooter
- August 10, 2023
- 1 min read
- 0