Paritosh Ramanan

Gradient sharing in distributed optimization can compromise client privacy, especially as convergence occurs, highlighting a critical vulnerability in collaborative frameworks.

Data isolation in decentralized operations can optimize chemical plant scheduling while keeping operational secrets safe, revealing surprising emissions dynamics.

Discovering hidden dependencies between black-box industrial systems just got easier: a new federated learning approach can pinpoint root causes without needing access to sensitive data or altering existing client models.