# Hierarchical Federated ADMM

arXiv: https://arxiv.org/abs/2409.18796 · HTML: https://mlsanity.com/paper/2409.18796/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021)
- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study](https://mlsanity.com/paper/1509.04210/) (2015)
- [Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking](https://mlsanity.com/paper/2205.13054/) (2022)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Scalable Hierarchical Over-the-Air Federated Learning](https://mlsanity.com/paper/2211.16162/) (2022)
- [Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity](https://mlsanity.com/paper/2308.01562/) (2023)
- [Delay-Aware Hierarchical Federated Learning](https://mlsanity.com/paper/2303.12414/) (2023)
- [Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity](https://mlsanity.com/paper/2401.01442/) (2024)
- [Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfer](https://mlsanity.com/paper/2305.01953/) (2023)
- [Federated Learning Using Three-Operator ADMM](https://mlsanity.com/paper/2211.04152/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees](https://mlsanity.com/paper/2505.08145/) (2025)
- [Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning](https://mlsanity.com/paper/2503.08869/) (2025)
- [Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning](https://mlsanity.com/paper/2602.02355/) (2026)
- [Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design](https://mlsanity.com/paper/2605.00931/) (2026)

## Abstract (arXiv)

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.