Abstract:
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the choice of optimizer on both the client and server sides significantly impacts training efficiency and model performance, especially under non-IID data distributions. Despite the existence of numerous optimizers, the absence of strong, consistent empirical evidence specific to federated environments makes it challenging to identify the most effective optimizer. Consequently, practitioners often rely on intuition and prior experience when choosing optimizers. This study provides comprehensive insights and practical guidelines for optimizer selection in federated learning frameworks. Beyond standard empirical risk minimization, min-max optimization is a fundamental framework in machine learning to model adversarial and robust problems, and its utility extends beyond traditional ML applications into econometrics and causal inference. One notable application is the Generalized Method of Moments (GMM), a widely used technique for causal effect estimation via Instrumental Variables (IV) analysis, which finds practical applications in important 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. If the data is sourced from scattered, decentralized clients, federated learning readily fits for training the models while promising data privacy. However, to our knowledge, no federated algorithm for either GMM or IV analysis exists to date. This study also includes a method for federated instrumental variables analysis (FedIV) via the federated deep generalized method of moments (FedDeepGMM) for non-iid data. We characterize an equilibrium of a federated zero-sum game to show that it 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.
