About
Contact & Profiles
A5069741306
Research Areas
- Privacy-Preserving Technologies in Data
- Stochastic Gradient Optimization Techniques
- Advanced Graph Neural Networks
University of Minnesota
2022
We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional approaches are inefficient for cross-silo problems because they require exchange messages gradient updates at every iteration, and raise security concerns over sharing such during learning. propose <i>Federated Stochastic Block Coordinate Descent (FedBCD)</i> algorithm,...
10.1109/tsp.2022.3198176
article
EN
IEEE Transactions on Signal Processing
2022-01-01
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