EAGER: Distributed Heterogeneous Data Analytics via Federated Learning

点击翻译按钮获取中文摘要

With the growth of devices in the Internet of Things (IoT), a huge amount of data are generated at the network edge. This provides valuable resources for learning insightful information and enabling intelligent applications such as, self-driving, video analytics, anomaly detection, etc. Federated learning (FL) is a promising technique that enables a large number of clients orchestrated by a central server to collaboratively learn a machine learning model without sharing data. However, the data owned by different devices are typically not independent and identically distributed (non-IID) due to different user preferences and usage patterns. Conventional FL methods fail to generalize well for most clients. In addition to data heterogeneity, system heterogeneity; that is, where clients have different computation and communication capabilities, is another critical challenge for FL development. Because the central server does not perform the aggregation until receiving all the clients’ updates, system heterogeneity significantly slows down the model training if the clients are randomly selected to participate in the training. The goal of this research is to develop a unified FL framework for addressing both data and system heterogeneity at the same time.This project will pave the foundations for properly handling data and system heterogeneity in FL with three integrated components: 1) unveiling essential reasons of performance degradation in FL with non-IID data; 2) exploring comprehensive principles to guide the client composition for FL with non-IID data; and 3) developing a unified FL method for addressing both data and system heterogeneity simultaneously, including a client utility function and a reinforcement learning based client composition method. This project will develop and train undergraduate and graduate researchers with comprehensive experience for developing FL systems, including recruiting minority and under-represented students. The outcome of this project will be incorporated in both new and existing undergraduate and graduate courses at Duke University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.