Advances, Systems and Applications
Table 1 Functional comparison with other works
From: Privacy-preserving federated learning based on partial low-quality data
Participant privacy protection | Prevent users from going offline or joining in the middle | All the data participated in the training | Threat model | Server type | |
|---|---|---|---|---|---|
SecProbe | \(\checkmark\) | \(\times\) | \(\checkmark\) | Semi honest | Single-Server |
PPFDL | \(\checkmark\) | \(\times\) | \(\checkmark\) | Semi honest | Dual-Servers |
EPPFL | \(\checkmark\) | \(\checkmark\) | \(\times\) | Semi honest | Single-Server |
PPFL-LQDP | \(\checkmark\) | \(\checkmark\) | \(\checkmark\) | Semi honest | Single-Server |