MLWiNS: Democratizing AI through Multi-Hop Federated Learning Over-the-Air

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Federated learning (FL) has emerged as a key technology for enabling next-generation privacy-preserving AI at-scale, where a large number of edge devices, e.g., mobile phones, collaboratively learn a shared global model while keeping their data locally to prevent privacy leakage. Enabling FL over wireless multi-hop networks, such as wireless community mesh networks and wireless Internet over satellite constellations, not only can augment AI experiences for urban mobile users, but also can democratize AI and make it accessible in a low-cost manner to everyone, including people in low-income communities, rural areas, under-developed regions, and disaster areas. The overall objective of this project is to develop a novel wireless multi-hop FL system with guaranteed stability, high accuracy and fast convergence speed. This project is expected to advance the design of distributed deep learning (DL) systems, to promote the understanding of the strong synergy between distributed computing and distributed networking, and to bridge the gap between the theoretical foundations of distributed DL and its real-life applications. The project will also provide unique interdisciplinary training opportunities for graduate and undergraduate students through both research work and related courses that the PIs will develop and offer. This project proposes to use concepts of federated learning and multi-agent reinforcement learning to provide optimal solutions for training DL models over wireless multi-hop networks that have communication constraints due to noisy and interference-rich wireless links. The main thrusts include: 1) developing a novel hierarchical FL system architecture with layered federated computation, semi-asynchronous model aggregation, and regularized objective function to significantly improve system scalability, communication efficiency, and stability; 2) fine-tuning the FL system via multi-agent reinforcement learning to maximize the FL accuracy with the minimum convergence time under the computing constraints of edge devices; 3) finding high-gain computation-light robust federated computing strategies for resource-constraint edge devices, including efficient DL model design and resource-aware model adaptation; and 4) developing an open-source wireless FL framework (OpenWFL) for fast prototyping, deploying, and evaluating the proposed FL algorithms in both an emulator and physical testbeds.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.