Machine Learning (ML) algorithms are generally designed for scenarios in
which all data is stored in one data center, where the training is performed.
However, in many applications, e.g., in the healthcare domain, the training
data is distributed among several entities, e.g., different hospitals or
patients' mobile devices/sensors. At the same time, transferring the data to a
central location for learning is certainly not an option, due to privacy
concerns and legal issues, and in certain cases, because of the communication
and computation overheads. Federated Learning (FL) is the state-of-the-art
collaborative ML approach for training an ML model across multiple parties
holding local data samples, without sharing them. However, enabling learning
from distributed data over such edge Internet of Things (IoT) systems (e.g.,
mobile-health and wearable technologies, involving sensitive personal/medical
data) in a privacy-preserving fashion presents a major challenge mainly due to
their stringent resource constraints, i.e., limited computing capacity,
communication bandwidth, memory storage, and battery lifetime. In this paper,
we propose a privacy-preserving edge FL framework for resource-constrained
mobile-health and wearable technologies over the IoT infrastructure. We
evaluate our proposed framework extensively and provide the implementation of
our technique on Amazon's AWS cloud platform based on the seizure detection
application in epilepsy monitoring using wearable technologies.

我们提出一种在物联网基础设施上，针对资源受限的移动健康和可穿戴技术的隐私保护边缘联邦学习框架，并通过在 Amazon 的 AWS 云平台上使用可穿戴技术监测癫痫的发作检测应用的实施评估了我们提议的框架。