Hierarchical Federated ADMM
Seyed Mohammad Azimi-Abarghouyi, Nicola Bastianello, Karl H., Johansson, Viktoria Fodor

TL;DR
This paper introduces a hierarchical federated learning framework based on ADMM, offering improved privacy, convergence, and accuracy over traditional gradient descent methods, with two novel algorithms for different layer configurations.
Contribution
The paper develops a new hierarchical federated learning framework using ADMM, including two algorithms that outperform conventional methods in privacy and performance.
Findings
ADMM-based hierarchical FL enhances privacy and accuracy.
Gradient descent performs well with limited local steps.
ADMM on both layers yields superior results.
Abstract
In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM). Within this framework, we propose two novel FL algorithms, which both use ADMM in the top layer: one that employs ADMM in the lower layer and another that uses the conventional gradient descent-based approach. The proposed framework enhances privacy, and experiments demonstrate the superiority of the proposed algorithms compared to the conventional algorithms in terms of learning convergence and accuracy. Additionally, gradient descent on the lower layer performs well even if the number of local steps is very limited, while ADMM on both layers lead to better performance otherwise.
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Taxonomy
TopicsDNA and Biological Computing · Algorithms and Data Compression · Advanced Database Systems and Queries
MethodsAlternating Direction Method of Multipliers
