Summary of Hierarchical Federated Admm, by Seyed Mohammad Azimi-abarghouyi et al.
Hierarchical Federated ADMM
by Seyed Mohammad Azimi-Abarghouyi, Nicola Bastianello, Karl H. Johansson, Viktoria Fodor
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel hierarchical federated learning (FL) framework based on the alternating direction method of multipliers (ADMM), departing from traditional gradient descent-based approaches. The proposed framework uses two novel FL algorithms that employ ADMM in the top layer, one with ADMM in the lower layer and another using conventional gradient descent. Experimental results demonstrate the superiority of these algorithms over conventional ones in terms of learning convergence and accuracy. Furthermore, the study shows that gradient descent on the lower layer performs well even with limited local steps, while ADMM on both layers leads to better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to learn together across different devices without sharing private information. Instead of using traditional methods, they use something called ADMM to make it more efficient and secure. The researchers compare their method to existing ones and show that it works better in many cases. They also find that if you don’t have much data on your device, the traditional method still works okay. |
Keywords
» Artificial intelligence » Federated learning » Gradient descent
