Summary of Resource-efficient Federated Multimodal Learning Via Layer-wise and Progressive Training, by Ye Lin Tun et al.
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training
by Ye Lin Tun, Chu Myaet Thwal, Minh N. H. Nguyen, Choong Seon Hong
First submitted to arxiv on: 22 Jul 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 The proposed LW-FedMML approach addresses the challenges of training deep neural networks on multimodal data in federated learning settings. By decomposing the training process into multiple stages, each focusing on a portion of the model, LW-FedMML significantly reduces memory and computational requirements. This is achieved by only exchanging trained model portions between clients and the central server, lowering communication costs. The results demonstrate that LW-FedMML can compete with conventional end-to-end federated multimodal learning (FedMML) while reducing resource burden on FL clients by up to 2.7 times for memory usage, 2.4 times for computational operations (FLOPs), and 2.3 times for total communication cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Combining different data types helps neural networks solve complex tasks better. To make this work with privacy-preserving techniques, we need to integrate multimodal learning with federated learning. However, training these models requires many resources, which is a challenge for devices operating on limited resources. We introduce LW-FedMML, an approach that breaks down the training process into smaller steps, reducing memory and computation needs. This makes it easier for devices to train the model and reduces the amount of data they need to share with each other. |
Keywords
» Artificial intelligence » Federated learning
