mlsanityData to 7 Oct 2026

Hierarchical Federated ADMM

arXiv 2409.18796 ↗First posted 27 Sep 2024cs.LG cs.AI cs.DCcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

14 of 21 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2021Convex optimizationcited by 2,479
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  5. 2022Federated Learning via Inexact ADMMcited by 44
  6. 2015Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Studycited by 19
  7. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  8. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  9. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  10. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  11. 2023Delay-Aware Hierarchical Federated Learningcited by 9
  12. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
2 more
  1. 2023Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfercited by 4
  2. 2022Federated Learning Using Three-Operator ADMMcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  2. 2025Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learningcited by 0
  3. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
  4. 2026Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Designcited by 0