Advancing fundamental understandings of wireless hierarchical federated learning

Wireless Engineering

By Joe McAdory

Xiaowen Gong, assistant professor in electrical and computer engineering, recently earned a five-year, $500,000 National Science Foundation (NSF) Early Faculty Career Development (CAREER) Award for his project, “Towards Efficient and Fast Hierarchical Federated Learning in Heterogeneous Wireless Edge Networks.”

“I’m so humbled to receive this honor,” said Gong, who specializes in wireless engineering. “There are so many other researchers who do excellent work but have not received this award. I view this award more as inspiration and encouragement for my future work.”

Gong’s project is expected to advance the fundamental understandings of, and develop adaptive and efficient algorithms and schemes for, computation and communication designs of wireless hierarchical federated learning — a machine learning approach that trains a machine learning system across multiple decentralized servers from distributed wireless devices with local data samples — while also addressing its challenges.

The NSF CAREER Award also focuses on the integration of research within education.

Gong proposes hands-on wireless and machine learning/AI projects for college students and outreach activities on robotics for K-12 students.

He was previously granted $589,889 by the NSF to provide hands-on research experiences in robotics and machine learning/AI to approximately 30 STEM middle-school teachers in underserved regions of Alabama during the next three years.