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.

翻译：本文研究了时变数据库下的差分隐私联邦学习（FL），深入探讨了年龄、准确性与差分隐私（DP）之间微妙的三方权衡关系。着眼于调度策略的潜在优势，我们提出了一个优化问题，旨在满足DP要求的同时，最小化聚合模型与无DP约束下所得模型之间的损失差异。为充分利用调度带来的益处，我们引入了一个与年龄相关的损失上界，进而发展出一种年龄感知的调度设计方案。仿真结果表明，相较于不考虑调度设计的经典DP联邦学习方法，我们所提方案展现出更优的性能。这项研究为理解联邦学习中年龄、准确性与DP之间的相互作用提供了新的见解，并对实际调度策略的制定具有指导意义。