Summary of Advances in Appfl: a Comprehensive and Extensible Federated Learning Framework, by Zilinghan Li et al.
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
by Zilinghan Li, Shilan He, Ze Yang, Minseok Ryu, Kibaek Kim, Ravi Madduri
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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) is a promising approach for collaborative model training while preserving data privacy. APPFL, an extensible framework and benchmarking suite, addresses heterogeneity and security concerns in FL. It offers user-friendly interfaces for integrating new algorithms or adapting to new applications. The paper demonstrates the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization. Additionally, the authors showcase the extensibility of APPFL through case studies in vertical, hierarchical, and decentralized FL. This framework is fully open-sourced on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) is a way for computers to work together and learn new things without sharing their personal data. A team of researchers created a special tool called APPFL that helps make sure this process happens safely and efficiently. They tested the tool with different types of data and scenarios, showing how it can be used in various situations. The goal is to allow computers to share knowledge and improve themselves without putting sensitive information at risk. |
Keywords
» Artificial intelligence » Federated learning
