Demo Abstract: HybriSim - A Hybrid Simulation System for Distributed Machine Learning with Mobility
Haoxiang Yu, Xi Zheng, Christine Julien · 2023
This paper introduces a novel hybrid simulation system (HybriSim) tailored for simulating distributed learning in mobile settings, such as those involving vehicles and pedestrians navigating through cities. Designed to be learning-method independent, the system is compatible with decentralized learning, federated learning, or a combination of the two. It has special relevance for decentralized learning systems that are sensitive to mobility patterns and rely on direct, device-to-device communication. Existing tools for evaluating resource-intensive tasks in opportunistic networks are either purely simulated, which may not accurately reflect system performance, or take the form of testbeds of real devices, which are difficult to scale to use cases involving huge numbers of devices, such as distributed learning. By integrating real devices with virtual simulated devices, HybriSim more accurately mirrors real-world performance and dynamics. This integration not only mitigates the biases associated with pure simulations but also resolves the deployment complexities of conducting simulations entirely on real devices. Our system sets a new benchmark for academic and industry researchers, facilitating more reliable and actionable insights into distributed learning systems in mobility contexts.