Summary of Distdd: Distributed Data Distillation Aggregation Through Gradient Matching, by Peiran Wang et al.
DistDD: Distributed Data Distillation Aggregation through Gradient Matching
by Peiran Wang, Haohan Wang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach within the federated learning framework is introduced in this paper, called DistDD. It reduces repetitive communication by distilling data directly on clients’ devices, unlike traditional federated learning which requires iterative model updates across nodes. The DistDD algorithm extracts a global distilled dataset through a one-time process, maintaining privacy standards while cutting down communication costs significantly. This allows for just-in-time parameter tuning and neural architecture search over FL without repeating the whole process multiple times. The paper provides a detailed convergence proof of the algorithm, demonstrating its mathematical stability and reliability for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to share information between devices called DistDD. This helps reduce how much data needs to be sent between devices by processing it on the device instead. It’s like a shortcut that saves time and keeps sensitive information private. The team tested this method with different types of data and showed that it works well even when there are mistakes or inconsistencies in the data. They also applied it to a specific task, searching for the best neural network architecture, and found it was very effective. |
Keywords
* Artificial intelligence * Federated learning * Neural network
