Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training

Understand

Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems.

• However, almost all existing works have to face large communication burdens and the risk of disruption if the central server fails.
• Only limited efforts have been used in a decentralized way but still suffers from inferior representation ability due to sharing the full model with its neighbors.
• Therefore, in this paper, we propose a personalized FL framework with a decentralized partial model training called DFedAlt.