Mobile apps, such as mHealth and wellness applications, can benefit from deep
learning (DL) models trained with mobile sensing data collected by smart phones
or wearable devices. However, currently there is no mobile sensing DL system
that simultaneously achieves good model accuracy while adapting to user
mobility behavior, scales well as the number of users increases, and protects
user data privacy. We propose Zone-based Federated Learning (ZoneFL) to address
these requirements. ZoneFL divides the physical space into geographical zones
mapped to a mobile-edge-cloud system architecture for good model accuracy and
scalability. Each zone has a federated training model, called a zone model,
which adapts well to data and behaviors of users in that zone. Benefiting from
the FL design, the user data privacy is protected during the ZoneFL training.
We propose two novel zone-based federated training algorithms to optimize zone
models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient
Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical
partitions through merging of neighboring zones or splitting of large zones
into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes
a zone model by incorporating the gradients derived from neighboring zones'
data. ZGD uses a self-attention mechanism to dynamically control the impact of
one zone on its neighbors. Extensive analysis and experimental results
demonstrate that ZoneFL significantly outperforms traditional FL in two models
for heart rate prediction and human activity recognition. In addition, we
developed a ZoneFL system using Android phones and AWS cloud. The system was
used in a heart rate prediction field study with 63 users for 4 months, and we
demonstrated the feasibility of ZoneFL in real-life.

该论文提出了一种基于区域的联邦学习系统（ZoneFL），旨在通过将地理空间划分为区域并针对用户数据和行为适应区域模型，同时保护用户数据隐私。该论文提出了两个新的区域级联训练算法（ZMS 和 ZGD），并证明了使用 ZoneFL 进行心率预测和人类活动识别可以获得更好的性能。