Efficient Federated Learning Framework on Heterogeneous Environment

Research Overview of the Federated Learning Paradigm

Research Overview

Our team aims to design promising solutions for future AI applications in Federated Learning (FL) systems, which enable distributed computing nodes to collaboratively train machine learning models without exposing their own data. We focus on solving the following challenging issues:

  • Heterogeneous Hardware & Data
  • Resource constraints
  • Expensive communication
  • Lack of participants

[1]. Edge Learning: the Enabling Technology for Distributed Big Data Analytics in the Edge. ACM Computing Surveys (TC), JCR-Q1

[2]. A Survey of Incentive Mechanism Design for Federated Learning. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), JCR-Q1