Summary of Feddcl: a Federated Data Collaboration Learning As a Hybrid-type Privacy-preserving Framework Based on Federated Learning and Data Collaboration, by Akira Imakura et al.
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
by Akira Imakura, Tetsuya Sakurai
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 to federated learning, called Federated Data Collaboration Learning (FedDCL), is introduced in this study. FedDCL combines federated learning with data collaboration analysis to enable integrated analysis of data held by multiple institutions without sharing raw data. The proposed framework solves communication challenges by having each user institution construct dimensionality-reduced intermediate representations and share them with neighboring institutions, which are then transformed into collaboration representations for federated learning between intra-group servers. This approach eliminates the need for iterative communication with the outside world and can be implemented in situations where continuous communication is extremely difficult. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Data Collaboration Learning (FedDCL) is a new way to do machine learning on data that’s spread across different places, like hospitals or companies. Normally, this kind of thing would require sharing all the data with everyone else, which can be bad for privacy. But FedDCL lets you keep your data private and still get really good results. It works by having each group make a special summary of their data and then share that with other groups. Then, they work together to figure out what the best solution is without sharing any actual data. This new way of doing things is important because it helps protect people’s privacy while still letting them do cool machine learning stuff. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
