167 citations · 262 across the 13 of their papers we have counts for
20 papers
Must the Communication Graph of MPC Protocols be an Expander?
Elette Boyle, Ran Cohen, Deepesh Data +1
Secure multiparty computation (MPC) on incomplete communication networks has been studied within two primary models: (1) Where a partial network is fixed a priori, and thus corrupt…
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
Antonious M. Girgis, Deepesh Data, Suhas Diggavi
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy.…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
Kaan Ozkara, Navjot Singh, Deepesh Data +1
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face…
On the Renyi Differential Privacy of the Shuffle Model
Antonious M. Girgis, Deepesh Data, Suhas Diggavi +2
The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model. In the shuffle model,…
QuPeL: Quantized Personalization with Applications to Federated Learning
Kaan Ozkara, Navjot Singh, Deepesh Data +1
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face…