FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning

Song Y, Wang Z, Zuazua Iriondo E (2025)

Publication Language: English

Publication Status: Submitted

Publication Type: Journal article

Future Publication Type: Journal article

Publication year: 2025

Journal

Neural Networks Elsevier

Book Volume: 181

Article Number: 106772

DOI: 10.1016/j.neunet.2024.106772

Abstract

Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. Recently developed FedADMM methods show great resilience to both data and system heterogeneity. However, they still suffer from performance deterioration if the hyperparameters are not carefully tuned. To address this issue, we propose an inexact and self-adaptive FedADMM algorithm, termed FedADMM-InSa. First, we design an inexactness criterion for the clients' local updates to eliminate the need for empirically setting the local training accuracy. This inexactness criterion can be assessed by each client independently based on its unique condition, thereby reducing the local computational cost and mitigating the undesirable straggle effect. The convergence of the resulting inexact ADMM is proved under the assumption of strongly convex loss functions. Additionally, we present a self-adaptive scheme that dynamically adjusts each client's penalty parameter, enhancing algorithm robustness by mitigating the need for empirical penalty parameter choices for each client. Extensive numerical experiments on both synthetic and real-world datasets have been conducted. As validated by some tests, our FedADMM-InSa algorithm improves model accuracy by 7.8% while reducing clients' local workloads by 55.7% compared to benchmark algorithms.

Authors with CRIS profile

Yongcun Song Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur) Ziqi Wang Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur) Enrique Zuazua Iriondo Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur)

How to cite

APA:

Song, Y., Wang, Z., & Zuazua Iriondo, E. (2025). FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning. Neural Networks, 181. https://doi.org/10.1016/j.neunet.2024.106772

MLA:

Song, Yongcun, Ziqi Wang, and Enrique Zuazua Iriondo. "FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning." Neural Networks 181 (2025).

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