Through MLWiNS, Intel and NSF will fund research with the goal of driving new wireless system and architecture design, increasing the utilization of sparse spectrum resources and enhancing distributed machine learning computation over wireless edge networks. Grant winners will conduct research across multiple areas of machine learning and wireless networking.
Key focus areas and project examples include the following:
- Reinforcement learning for wireless networks: Research teams from the University of Virginia and Penn State University will study reinforcement learning for optimizing wireless network operation, focusing on tackling convergence issues, leveraging knowledge-transfer methods to reduce the amount of training data necessary, and bridging the gap between model-based and model-free reinforcement learning through an episodic approach.
- Federated learning for edge computing:
- Researchers from the University of North Carolina at Charlotte will explore methods to speed up multi-hop federated learning over wireless communications, allowing multiple groups of devices to collaboratively train a shared global model while keeping their data local and private. Unlike classical federated learning systems that utilize single-hop wireless communications, multi-hop system updates need to go through multiple noisy and interference-rich wireless links, which can result in slower updates. Researchers aim to overcome this challenge by developing a novel wireless multi-hop federated learning system with guaranteed stability, high accuracy and a fast convergence speed by systematically addressing the challenges of communication latency, and system and data heterogeneity.
- Researchers from the Georgia Institute of Technology will analyze and design federated and collaborative machine-learning training and inference schemes for edge computing, with the goal of increasing efficiency over wireless networks. The team will address challenges with real-time deep learning at the edge, including limited and dynamic wireless channel bandwidth, unevenly distributed data across edge devices and on-device resource constraints.
- Research from the University of Southern California and the University of California, Berkeley will focus on a coding-centric approach to enhance federated learning over wireless communications. Specifically, researchers will work to tackle the challenges of dealing with non-independent and identically distributed data, and heterogeneous resources at the wireless edge, and minimizing upload bandwidth costs from users, while emphasizing issues of privacy and security when learning from distributed data.