Deepesh Data

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 57

Top Topics

High Dimensions

Stochastic Gradient Descent

Heterogeneous Data

Top Venues

IEEE Trans. Inf. Theory

IEEE J. Sel. Areas Inf. Theory

Publications

Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. ICLR (2023)
Deepesh Data, Suhas N. Diggavi
Byzantine-Resilient High-Dimensional Federated Learning. IEEE Trans. Inf. Theory 69 (10) (2023)
Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
Must the Communication Graph of MPC Protocols be an Expander? J. Cryptol. 36 (3) (2023)
Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
Must the Communication Graph of MPC Protocols be an Expander? CoRR (2023)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. IEEE Trans. Autom. Control. 68 (2) (2023)
Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy. CoRR (2022)
Yanwen Mao, Deepesh Data, Suhas N. Diggavi, Paulo Tabuada
Decentralized Learning Robust to Data Poisoning Attacks. CDC (2022)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
Distributed User-Level Private Mean Estimation. ISIT (2022)
Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran
Flexible Accuracy for Differential Privacy. AISTATS (2022)
Deepesh Data, Suhas N. Diggavi
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. ISIT (2021)
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 Horvath, 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
A Field Guide to Federated Optimization. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
Differentially Private Federated Learning with Shuffling and Client Self-Sampling. ISIT (2021)
Deepesh Data, Linqi Song, Suhas N. Diggavi
Data Encoding for Byzantine-Resilient Distributed Optimization. IEEE Trans. Inf. Theory 67 (2) (2021)
Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. NeurIPS (2021)
Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
QuPeL: Quantized Personalization with Applications to Federated Learning. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Renyi Differential Privacy of the Shuffle Model. CoRR (2021)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. ISIT (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. NeurIPS (2021)
Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory 2 (1) (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Differential Privacy in Federated Learning. AISTATS (2021)
Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran
Flexible Accuracy for Differential Privacy. CoRR (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
On the Rényi Differential Privacy of the Shuffle Model. CCS (2021)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. IEEE J. Sel. Areas Inf. Theory 2 (3) (2021)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning. CoRR (2021)
Deepesh Data, Suhas N. Diggavi
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. ICML (2021)
Deepesh Data, Suhas N. Diggavi
On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent. ISIT (2020)
Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran
Interactive Secure Function Computation. IEEE Trans. Inf. Theory 66 (9) (2020)
Deepesh Data, Suhas N. Diggavi
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. CoRR (2020)
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
Successive Refinement of Privacy. CoRR (2020)
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory 1 (3) (2020)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. CoRR (2020)
Deepesh Data, Suhas N. Diggavi
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. CoRR (2020)
Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations. IEEE J. Sel. Areas Inf. Theory 1 (1) (2020)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
Hiding Identities: Estimation Under Local Differential Privacy. ISIT (2020)
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR (2020)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. CDC (2020)
Deepesh Data, Suhas N. Diggavi
Byzantine-Tolerant Distributed Coordinate Descent. ISIT (2019)
Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations. CoRR (2019)
Deepesh Data, Linqi Song, Suhas N. Diggavi
Data Encoding for Byzantine-Resilient Distributed Optimization. CoRR (2019)
Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization. CoRR (2019)
Deepesh Data, Linqi Song, Suhas N. Diggavi
Data Encoding Methods for Byzantine-Resilient Distributed Optimization. ISIT (2019)
Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. NeurIPS (2019)
Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran
Interactive Secure Function Computation. CoRR (2018)
Deepesh Data, Linqi Song, Suhas N. Diggavi
Data Encoding for Byzantine-Resilient Distributed Gradient Descent. Allerton (2018)
Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
Must the Communication Graph of MPC Protocols be an Expander? IACR Cryptol. ePrint Arch. 2018 (2018)
Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
Must the Communication Graph of MPC Protocols be an Expander? CRYPTO (3) (2018)
Deepesh Data, Manoj Prabhakaran
Towards Characterizing Securely Computable Two-Party Randomized Functions. Public Key Cryptography (1) (2018)
Deepesh Data, Vinod M. Prabhakaran
Secure computation of randomized functions: Further results. ITW (2017)
Deepesh Data, Vinod M. Prabhakaran
Secure Computation of Randomized Functions: Further Results. CoRR (2017)
Deepesh Data, Manoj Prabhakaran
Towards Characterizing Securely Computable Two-Party Randomized Functions. IACR Cryptol. ePrint Arch. 2017 (2017)
Deepesh Data
Secure Computation of Randomized Functions. CoRR (2016)
Deepesh Data
Secure computation of randomized functions. ISIT (2016)
Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran
Communication and Randomness Lower Bounds for Secure Computation. IEEE Trans. Inf. Theory 62 (7) (2016)
Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran
Communication and Randomness Lower Bounds for Secure Computation. CoRR (2015)
Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran
On the Communication Complexity of Secure Computation. IACR Cryptol. ePrint Arch. 2015 (2015)
Deepesh Data, Vinod M. Prabhakaran
On coding for secure computing. ISIT (2015)
Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran
How to Securely Compute the Modulo-Two Sum of Binary Sources. CoRR (2014)
Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran
On the Communication Complexity of Secure Computation. CRYPTO (2) (2014)
Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran
How to securely compute the modulo-two sum of binary sources. ITW (2014)
Deepesh Data, Vinod M. Prabhakaran
Communication requirements for secure computation. Allerton (2013)
Deepesh Data, Vinod M. Prabhakaran, Manoj Prabhakaran
On the Communication Complexity of Secure Computation. CoRR (2013)