2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-12.4
Paper Title DP-SIGNSGD: WHEN EFFICIENCY MEETS PRIVACY AND ROBUSTNESS
Authors Lingjuan Lyu, Ant Group, Singapore
SessionMLSP-12: Federated Learning 1
LocationGather.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
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.