Summary of Fedtrans: Efficient Federated Learning Via Multi-model Transformation, by Yuxuan Zhu et al.
FedTrans: Efficient Federated Learning via Multi-Model Transformation
by Yuxuan Zhu, Jiachen Liu, Mosharaf Chowdhury, Fan Lai
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Federated learning (FL) enables the training of machine learning (ML) models across millions of edge client devices. However, personalizing ML models for FL clients is challenging due to heterogeneity in client data, device capabilities, and scale, making individualized model exploration expensive. State-of-the-art FL solutions personalize globally trained models or concurrently train multiple models, but they often compromise on accuracy and incur significant training costs. This paper addresses this challenge by introducing a novel approach that trains ML models for FL clients while reducing training costs and improving model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train machine learning models on many devices, like smartphones or computers. However, it’s hard to make each device have its own special model because of differences in the data they collect, what kind of devices they are, and how many there are. Existing solutions either use one model for all devices or train multiple models at once, but this can lead to less accurate models and a lot of extra work. This paper tries to fix this problem by developing a new way to train models that is faster and more accurate. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
