Summary of Expediting In-network Federated Learning by Voting-based Consensus Model Compression, By Xiaoxin Su et al.
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
by Xiaoxin Su, Yipeng Zhou, Laizhong Cui, Song Guo
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this research paper, the authors propose a novel approach to federated learning (FL) called Federated Learning in-network Aggregation with Compression (FediAC). FL is a method for training machine learning models on distributed data without sharing the raw data. The authors aim to improve the communication speed and reduce memory consumption by deploying a programmable switch (PS) instead of a parameter server. They design FediAC as a two-phase algorithm: client voting and model aggregating. In the first phase, clients report their significant model updates to the PS, which estimates global significant updates. In the second phase, clients upload these updates for aggregation. The authors claim that FediAC outperforms existing methods in terms of model accuracy and communication traffic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper is about making it faster and more efficient to train AI models on many devices without sharing their data. It proposes a new way to do this called Federated Learning in-network Aggregation with Compression (FediAC). FediAC is better than existing methods because it uses less memory and sends less information between devices. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
