mlsanityData to 14 Oct 2026

Federated Instrumental Variable Analysis via Federated Generalized Method of Moments

arXiv 2505.21012 ↗First posted 27 May 2025cs.LG cs.AI math.OCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 34 of its 70 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

34 of 70 references.

References · 34

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22 more
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Citing papers per year: 2026: 1.

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  1. 2026A Survey on Federated Causal Discovery and Inferencecited by 0