Separation of Powers in Federated Learning (Poster Paper)
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu, Hani Jamjoom, K. R. Jayaram, Enriquillo Valdez, Ashish Kumar Verma · 2021
In federated learning (FL), model updates from mutually distrusting parties are aggregated in a centralized fusion server. The concentration of model updates simplifies FL's model building process, but might lead to unforeseeable information leakage. This problem has become acute due to recent FL attacks that can reconstruct large fractions of training data from ostensibly "sanitized" model updates.