  This paper explores differentially-private federated learning (FL) across
time-varying databases, delving into a nuanced three-way tradeoff involving
age, accuracy, and differential privacy (DP). Emphasizing the potential
advantages of scheduling, we propose an optimization problem aimed at meeting
DP requirements while minimizing the loss difference between the aggregated
model and the model obtained without DP constraints. To harness the benefits of
scheduling, we introduce an age-dependent upper bound on the loss, leading to
the development of an age-aware scheduling design. Simulation results
underscore the superior performance of our proposed scheme compared to FL with
classic DP, which does not consider scheduling as a design factor. This
research contributes insights into the interplay of age, accuracy, and DP in
federated learning, with practical implications for scheduling strategies.
