We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
IMPROVING THE LEARNING PERFORMANCE OF CLIENT'S LOCAL DISTRIBUTION IN CYCLIC FEDERATED LEARNING.
- Authors
LI KANG; BIN LUO; JIANJUN HUANG
- Abstract
Cyclic federated learning based on distribution information sharing and knowledge distillation (CFL_DS_KD) aims to address the challenges of non-iid data distribution and reduce communication requirements. However, when client data is extremely heterogeneous and scarce, it becomes challenging for clients to fully learn the distribution of local data using GANs, thereby affecting the overall model performance. To overcome this limitation, we propose a transfer learning approach where clients first pretrain their generators on a source domain and then fine-tune them on their local datasets. Our results on the classification of Alzheimer’s disease demonstrate that this method effectively improves client distribution learning performance and enhances the overall model performance.
- Subjects
FEDERATED learning; DATA distribution; ALZHEIMER'S disease
- Publication
Image Analysis & Stereology, 2024, Vol 43, Issue 1, p1
- ISSN
1580-3139
- Publication type
Article
- DOI
10.5566/ias.3131