Federated Knowledge Transfer for Heterogeneous Visual Models
Wenzhe Li, Zirui Zhu, Tianchi Huang, Lifeng Sun, Chun Yuan · 2022
Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables collaborative training of machine learning models among multiple participants. However, despite recent progress, existing federated learning systems can still not handle heterogeneous models. For instance, candidate clients with heterogeneous models are inaccessible to the established federated system. And within the federated system, local models are forbidden to be updated to become heterogeneous models, even though the updated models work better.