An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation
Year: 2023
Type: preprint
Abstract: Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing. People's health information is collected by edge devices such as smartphones and wearable bands for further analysis on servers, then send back suggestions and alerts for abnormal conditions. The recent emergence of federated learning allows users to train private data on local devices while updating models collaboratively. However, the heterogeneous distribution of the health condition data may lead to significant risks to model performance due to class imbalance. Meanwhile, as FL training is powered by sharing gradients only with the server, training data is almost inaccessible. The conventional solutions to class imbalance do not work for federated learning. In this work, we propose a new federated learning framework FedImT, dedicated to addressing the challenges of class imbalance in federated learning scenarios. FedImT contains an online scheme that can estimate the data composition during each round of aggregation, then introduces a self-attenuating iterative equivalent to track variations of multiple estimations and promptly tweak the balance of the loss computing for minority classes. Experiments demonstrate the effectiveness of FedImT in solving the imbalance problem without extra energy consumption and avoiding privacy risks.
Source: arXiv (Cornell University)
Authors: Yan, Wenhao, Li, He, Ota, Kaoru, Dong, Mianxiong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Ultra low power communication for next-generation wireless systems, Achieving 1-week Lifetime for 100/1000/10000 Meter Emergency Communications, Building Disaster Resilience IoT System for Both Normal and Emergency Use, Research and Development of Ultra-Low Power Consumption IoT Systems, G8870756735, AI-empowered Point Cloud Video Streaming
OpenAlex ID: W4387929496