Definition ∞ Gradient sharing privacy refers to techniques that protect the confidentiality of individual data contributions when sharing model gradients in federated learning systems. Instead of sharing raw data, only aggregated or perturbed gradients are exchanged, preventing reconstruction of sensitive local information. This method helps maintain user privacy while still enabling collaborative model training. It addresses the challenge of data leakage during distributed AI processes.
Context ∞ Gradient sharing privacy is a central concern in federated learning research, as even aggregated gradients can sometimes reveal sensitive data patterns. Ongoing efforts focus on improving differential privacy mechanisms and secure aggregation protocols to enhance confidentiality. This field is critical for applications where data sovereignty and user privacy are paramount, such as healthcare and finance.
Blockchain Knowledge
Cryptocurrency Foundation
Cryptospace Newsfeed
