Summary of Privacy-preserving Edge Federated Learning For Intelligent Mobile-health Systems, by Amin Aminifar et al.
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
by Amin Aminifar, Matin Shokri, Amir Aminifar
First submitted to arxiv on: 9 May 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 This paper proposes a Federated Learning (FL) framework for training machine learning models on distributed data from mobile-health and wearable technologies, while preserving privacy and addressing resource constraints. The authors focus on scenarios where sensitive personal/medical data is involved, such as seizure detection in epilepsy monitoring. They develop a novel approach to enable FL over edge IoT systems, considering limited computing capacity, communication bandwidth, memory storage, and battery lifetime. The proposed framework is evaluated extensively and implemented on Amazon’s AWS cloud platform. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem: how to train machine learning models when data is spread across many places, like hospitals or people’s phones, without sharing the data. This is important because some data is very personal, like medical information. The authors create a new way to do this Federated Learning (FL) thing on devices that don’t have much power, storage, or battery life. They test it and make it work on Amazon’s cloud platform. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
