Summary of Decentralized Personalized Federated Learning Based on a Conditional Sparse-to-sparser Scheme, by Qianyu Long et al.
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme
by Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos, Daning Bi
First submitted to arxiv on: 24 Apr 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 This paper proposes a novel decentralized federated learning (DFL) method called DA-DPFL that addresses the limitations of existing DFL approaches. By initializing with a subset of model parameters, DA-DPFL progressively reduces training costs via dynamic aggregation and achieves substantial energy savings while retaining adequate information during critical learning periods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decentralized Federated Learning is a way for devices to learn together without sharing their data. It’s like a team working on a project without sending the whole project to one person. But, this method can be slow because devices need to communicate with each other and train models. Researchers have been trying to make it faster, but some methods focus too much on communication and ignore how devices are trained. The new method, DA-DPFL, starts by only using a little bit of the model’s information, then adds more as needed. This helps save energy while still learning well. |
Keywords
» Artificial intelligence » Federated learning
