Prof. Dr. Zhu Han

  • Department: ECE Department and CS Department
  • University: University of Houston
  • Country: USA

Abstract: In recent years, mobile devices are equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications, e.g., for augmented reality, Internet of Things, and vehicular networks. Traditional cloud- based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile edge computing (MEC) has been proposed to bring intelligence closer to the edge, where data is originally generated. However, conventional edge ML technologies still require personal data to be shared with edge servers. Recently, in light of increasingly privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. The end devices then send the local model updates instead of raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables ML for mobile edge network optimization. However, in a large-scale and complex mobile edge network, FL still faces the implementation challenges with regard to communication costs and resource allocation. In this talk, we begin with an introduction to the background and fundamentals of FL. Then, we discuss several potential challenges for FL implementation. In addition, we study the extension to federated analysis.