Explore a conference talk from PPML 2022 Contributed Talks I focusing on MicroFedML, a novel approach to privacy-preserving federated learning designed for small weights. Gain insights into cutting-edge techniques for maintaining data privacy in machine learning applications, particularly in resource-constrained environments. Discover how this innovative method addresses challenges in federated learning while ensuring confidentiality and efficiency.
MicroFedML: Privacy Preserving Federated Learning for Small Weights
Overview
Syllabus
PPML 2022 Contributed Talks I (part II)
Taught by
TheIACR
Related Courses
-
Privacy-Preserving Machine Learning Workshop 2023 - Contributed Talks Session II
-
Privacy-Preserving Machine Learning - Invited and Contributed Talks
-
Privacy-Preserving Machine Learning Workshop - Invited and Contributed Talks
-
Privacy Preserving Aggregate Statistics via Boolean Shares - Part 3
-
Samplable Anonymous Aggregation for Private Federated Data Analysis
-
Low-Communication Algorithms for Private Federated Data Analysis - Invited Talk
