  Recent works have brought attention to the vulnerability of Federated
Learning (FL) systems to gradient leakage attacks. Such attacks exploit
clients' uploaded gradients to reconstruct their sensitive data, thereby
compromising the privacy protection capability of FL. In response, various
defense mechanisms have been proposed to mitigate this threat by manipulating
the uploaded gradients. Unfortunately, empirical evaluations have demonstrated
limited resilience of these defenses against sophisticated attacks, indicating
an urgent need for more effective defenses. In this paper, we explore a novel
defensive paradigm that departs from conventional gradient perturbation
approaches and instead focuses on the construction of robust data. Intuitively,
if robust data exhibits low semantic similarity with clients' raw data, the
gradients associated with robust data can effectively obfuscate attackers. To
this end, we design Refiner that jointly optimizes two metrics for privacy
protection and performance maintenance. The utility metric is designed to
promote consistency between the gradients of key parameters associated with
robust data and those derived from clients' data, thus maintaining model
performance. Furthermore, the privacy metric guides the generation of robust
data towards enlarging the semantic gap with clients' data. Theoretical
analysis supports the effectiveness of Refiner, and empirical evaluations on
multiple benchmark datasets demonstrate the superior defense effectiveness of
Refiner at defending against state-of-the-art attacks.

Machine Learning;Cryptography and Security