S030 - Data-Free One-Shot Federated Regression: An Application to Bone Age Assessment
Zhou Zheng, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori
We consider a novel problem setting: data-free one-shot federated regression. This setting aims to prepare a global model through a single round of communication without relying on auxiliary information, e.g., proxy datasets. To address this problem, we propose a practical framework that consists of three stages: local training, data synthesizing, and knowledge distillation, and demonstrate its efficacy with an application to bone age assessment. We conduct validation under independent and identical distribution (IID) and non-IID settings while considering both model homogeneity and heterogeneity. Validation results show that our method surpasses FedAvgOneShot by a large margin and sometimes even outperforms the proxy-data-dependent approach FedOneShot.
Schedule: Wednesday, July 12: Virtual poster session - 8:00–9:00
Poster location: Virtual only