Deepesh Data

According to our database, Deepesh Data authored at least 38 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Byzantine-Resilient High-Dimensional Federated Learning.

Deepesh Data, Suhas N. Diggavi

IEEE Trans. Inf. Theory, October, 2023

Must the Communication Graph of MPC Protocols be an Expander?

Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek

J. Cryptol., July, 2023

SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization.

Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi

IEEE Trans. Autom. Control., February, 2023

A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy.

Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy.

Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

CoRR, 2022

Distributed User-Level Private Mean Estimation.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2022

Decentralized Learning Robust to Data Poisoning Attacks.

Yanwen Mao, Deepesh Data, Suhas N. Diggavi, Paulo Tabuada

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Flexible Accuracy for Differential Privacy.

Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Data Encoding for Byzantine-Resilient Distributed Optimization.

Deepesh Data, Linqi Song, Suhas N. Diggavi

IEEE Trans. Inf. Theory, 2021

SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization.

Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi

IEEE J. Sel. Areas Inf. Theory, 2021

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh

IEEE J. Sel. Areas Inf. Theory, 2021

A Field Guide to Federated Optimization.

Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

CoRR, 2021

QuPeL: Quantized Personalization with Applications to Federated Learning.

Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi

CoRR, 2021

QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning.

Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially Private Federated Learning with Shuffling and Client Self-Sampling.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2021

Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data.

Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2021

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data.

Deepesh Data, Suhas N. Diggavi

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Rényi Differential Privacy of the Shuffle Model.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

Shuffled Model of Differential Privacy in Federated Learning.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Interactive Secure Function Computation.

Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran

IEEE Trans. Inf. Theory, 2020

Successive Refinement of Privacy.

Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi

IEEE J. Sel. Areas Inf. Theory, 2020

Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations.

Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi

IEEE J. Sel. Areas Inf. Theory, 2020

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh

CoRR, 2020

Hiding Identities: Estimation Under Local Differential Privacy.

Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2020

On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent.

Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2020

2019

SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization.

Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi

CoRR, 2019

Data Encoding Methods for Byzantine-Resilient Distributed Optimization.

Deepesh Data, Linqi Song, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2019

Byzantine-Tolerant Distributed Coordinate Descent.

Deepesh Data, Suhas N. Diggavi

Proceedings of the IEEE International Symposium on Information Theory, 2019

2018

Data Encoding for Byzantine-Resilient Distributed Gradient Descent.

Deepesh Data, Linqi Song, Suhas N. Diggavi

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017

Towards Characterizing Securely Computable Two-Party Randomized Functions.

Deepesh Data, Manoj Prabhakaran

IACR Cryptol. ePrint Arch., 2017

Secure computation of randomized functions: Further results.

Deepesh Data, Vinod M. Prabhakaran

Proceedings of the 2017 IEEE Information Theory Workshop, 2017

2016

Communication and Randomness Lower Bounds for Secure Computation.

Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran

IEEE Trans. Inf. Theory, 2016

Secure computation of randomized functions.

Deepesh Data

Proceedings of the IEEE International Symposium on Information Theory, 2016

2015

On the Communication Complexity of Secure Computation.

Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran

IACR Cryptol. ePrint Arch., 2015

On coding for secure computing.

Deepesh Data, Vinod M. Prabhakaran

Proceedings of the IEEE International Symposium on Information Theory, 2015

2014

How to securely compute the modulo-two sum of binary sources.

Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran

Proceedings of the 2014 IEEE Information Theory Workshop, 2014

2013

Communication requirements for secure computation.

Deepesh Data, Vinod M. Prabhakaran

Proceedings of the 51st Annual Allerton Conference on Communication, 2013