Specifically, we lay the foundation within the Bayesian framework.

We establish the first generalization error bound of FedZO under the Lipschitz continuity and smoothness conditions.

However, existing music datasets suffer from limited size, accessibility, and lack of audio resources.

Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning.