| Paper ID | MLSP-12.4 |
| Paper Title |
DP-SIGNSGD: WHEN EFFICIENCY MEETS PRIVACY AND ROBUSTNESS |
| Authors |
Lingjuan Lyu, Ant Group, Singapore |
| Session | MLSP-12: Federated Learning 1 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-DFED] Distributed/Federated learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Federated learning (FL) has emerged as a promising collab- oration paradigm by enabling a multitude of parties to con- struct a joint model without exposing their private training data. Three main challenges in FL are efficiency, privacy, and robustness. The recently proposed SIGNSGD with majority vote shows a promising direction to deal with efficiency and Byzantine robustness. However, there is no guarantee that SIGNSGD is privacy-preserving. In this paper, we bridge this gap by presenting an improved method called DP-SIGNSGD, which can enjoy all the aforementioned properties. We also present an error-feedback variant of the proposed DP-SIGNSGD which further improves the learning performance in FL. We experimentally demonstrate the effectiveness of our proposed methods with extensive experiments on the image datasets. |
