Noah Guzinski

Mitigating Gradient Leakage in Asynchronous Federated Learning via Orthogonal Obfuscation

The Federated Learning (FL) paradigm, while promising for decentralized training, remains susceptible to privacy breaches via gradient leakage. While numerous defenses have been established for synchronous systems, these mechanisms often rely on batched aggregation to mask individual contributions, a property absent in...