This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.
Original paper

Achieving security and privacy in federated learning systems: Survey, research challenges and future directions

Volume: 106, Pages: 104468 - 104468
Published: Sep 17, 2021
Abstract
Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak...
Paper Details
Title
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
Published Date
Sep 17, 2021
Volume
106
Pages
104468 - 104468
© 2025 Pluto Labs All rights reserved.
Step 1. Scroll down for details & analytics related to the paper.
Discover a range of citation analytics, paper references, a list of cited papers, and more.