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.

探索差分隐私联邦学习在时变数据库中的应用，着重讨论关于年龄、准确性和差分隐私之间的三重平衡，并提出了一个优化问题，旨在满足差分隐私要求的同时最小化聚合模型和不考虑差分隐私约束下获得的模型之间的损失差异。通过引入基于年龄的损失上界来充分利用调度的优势，进而提出了一种基于年龄的调度设计。仿真结果表明，我们提出的方案在性能上优于不考虑调度作为设计因素的经典差分隐私联邦学习策略，该研究对联邦学习中年龄、准确性和差分隐私的相互作用提供了深入见解，并对调度策略具有实际意义。