提出联邦工具变量分析方法，实现跨机构隐私保护下的因果推断。
Federated Instrumental Variable Analysis via Federated Generalized Method of Moments
- 将广义矩估计与联邦学习结合，设计联邦极小极大优化框架。
- 理论证明联邦解能一致估计各客户端的局部矩条件。
- 适用于医疗、经济等数据分散且需隐私保护的场景。
工具变量（IV）分析在医疗和消费者经济学等领域具有重要意义。在高维场景下，基于深度神经网络的广义矩估计（GMM）提供了高效解决方案。面对来自分散客户端的非独立同分布数据，联邦学习能够在保障数据隐私的同时训练模型。然而，截至目前，尚无针对GMM或IV分析的联邦算法。本文提出通过联邦广义矩估计（FedGMM）实现联邦工具变量分析（FedIV）。我们将FedGMM建模为一个由联邦非凸非凹极小极大优化问题定义的联邦零和博弈，并使用联邦梯度下降上升算法（FedGDA）求解。一个关键挑战在于理论上刻画联邦局部最优性。为此，我们通过FedGDA极限点给出了客户端局部均衡的存在性与性质。结果表明，联邦解能一致估计每个参与客户端的局部矩条件。实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Instrumental variables (IV) analysis is an important applied tool for areas such as healthcare and consumer economics. For IV analysis in high-dimensional settings, the Generalized Method of Moments (GMM) using deep neural networks offers an efficient approach. With non-i.i.d. data sourced from scattered decentralized clients, federated learning is a popular paradigm for training the models while promising data privacy. However, to our knowledge, no federated algorithm for either GMM or IV analysis exists to date. In this work, we introduce federated instrumental variables analysis (FedIV) via federated generalized method of moments (FedGMM). We formulate FedGMM as a federated zero-sum game defined by a federated non-convex non-concave minimax optimization problem, which is solved using federated gradient descent ascent (FedGDA) algorithm. One key challenge arises in theoretically characterizing the federated local optimality. To address this, we present properties and existence results of clients' local equilibria via FedGDA limit points. Thereby, we show that the federated solution consistently estimates the local moment conditions of every participating client. The proposed algorithm is backed by extensive experiments to demonstrate the efficacy of our approach.
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