  We propose a novel differentially private algorithm for online federated
learning that employs temporally correlated noise to improve the utility while
ensuring the privacy of the continuously released models. To address challenges
stemming from DP noise and local updates with streaming noniid data, we develop
a perturbed iterate analysis to control the impact of the DP noise on the
utility. Moreover, we demonstrate how the drift errors from local updates can
be effectively managed under a quasi-strong convexity condition. Subject to an
$(\epsilon, \delta)$-DP budget, we establish a dynamic regret bound over the
entire time horizon that quantifies the impact of key parameters and the
intensity of changes in dynamic environments. Numerical experiments validate
the efficacy of the proposed algorithm.
