In federated learning (FL), clients cooperatively train a global model without revealing their raw data but gradients or parameters, while the local information can still be disclosed from local outputs transmitted to the parameter server. With such privacy concerns, a client may overly add artificial noise to his local updates to compromise the global model training, and we prove the selfish noise adding leads to an infinite price of anarchy (PoA). This paper proposes a novel pricing mechanism to regulate privacy-sensitive clients without verifying their parameter updates, unlike existing privacy mechanisms that assume the server's full knowledge of added noise. Without knowing the ground truth, our mechanism reaches the social optimum to best balance the global training error and privacy loss, according to the difference between a client's updated parameter and all clients' average parameter. We also improve the FL convergence bound by refining the aggregation rule at the server to account for different clients' noise variances. Moreover, we extend our pricing scheme to fit incomplete information of clients' privacy sensitivities, ensuring their truthful type reporting and the system's ex-ante budget balance. Simulations show that our pricing scheme greatly improves the system performance especially when clients have diverse privacy sensitivities.
翻译:在联合学习(FL)中,客户合作培训了一个全球模型,而没有透露原始数据,只是梯度或参数,而当地信息仍然可以从传送到参数服务器的本地产出中披露。由于这种隐私问题,客户可能会在本地更新时过度增加人为噪音,从而损害全球模型培训,我们证明自私的噪音会增加无限的无政府状态(PoA ) 。本文建议建立一个新的定价机制,在不核实参数更新的情况下监管隐私敏感客户,而不必核查参数更新,与假设服务器完全了解新增噪音的现有隐私机制不同。在不了解地面真相的情况下,我们的机制达到了社会最佳社会最佳水平,以根据客户更新参数和所有客户平均参数之间的差异,最佳平衡全球培训错误和隐私损失。我们还改进了FL的趋同,改进了服务器的汇总规则,以考虑到不同客户的噪音差异。此外,我们扩大了我们的定价计划,以适应客户隐私敏感度的不完整信息,确保客户的诚实型式报告和系统前安全预算平衡。模拟显示我们的定价计划大大改进了系统业绩,特别是在客户具有不同隐私敏感度时。</s>