On content modification attacks in bilateral teleoperation systems. - Yimeng Dong; Nirupam Gupta; Nikhil Chopra (ACC, 2016)

Confidentiality in distributed average information consensus. - Nirupam Gupta; Nikhil Chopra (CDC, 2016)

Robustness of distributive double-integrator consensus to loss of graph connectivity. - Nirupam Gupta; Yimeng Dong; Nikhil Chopra (ACC, 2017)

Model-Based Encryption: Privacy of States in Networked Control Systems. - Nirupam Gupta; Nikhil Chopra (Allerton, 2018)

Information-Theoretic Privacy in Distributed Average Consensus. - Nirupam Gupta; Jonathan Katz; Nikhil Chopra (CoRR, 2018)

Privacy in Distributed Multi-Agent Collaboration: consensus and Optimization. - Nirupam Gupta (2018)

Byzantine Fault-Tolerant Parallelized Stochastic Gradient Descent for Linear Regression. - Nirupam Gupta; Nitin H. Vaidya (Allerton, 2019)

Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs. - Nirupam Gupta; Jonathan Katz; Nikhil Chopra (ACC, 2019)

Byzantine Fault Tolerant Distributed Linear Regression. - Nirupam Gupta; Nitin H. Vaidya (CoRR, 2019)

Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization. - Nirupam Gupta; Nikhil Chopra (CoRR, 2019)

Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning. - Nirupam Gupta; Nitin H. Vaidya (CoRR, 2019)

Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (ACC, 2020)

Fault-Tolerance in Distributed Optimization: The Case of Redundancy. - Nirupam Gupta; Nitin H. Vaidya (PODC, 2020)

Resilience in Collaborative Optimization: Redundant and Independent Cost Functions. - Nirupam Gupta; Nitin H. Vaidya (CoRR, 2020)

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 2020)

Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE). - Nirupam Gupta; Shuo Liu 011; Nitin H. Vaidya (CoRR, 2020)

Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (CoRR, 2020)

Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 2020)

False Data Injection Attacks in Bilateral Teleoperation Systems. - Yimeng Dong; Nirupam Gupta; Nikhil Chopra (IEEE Trans. Control. Syst. Technol., 2020)

Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (ACC, 2021)

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination. - Nirupam Gupta; Shuo Liu 011; Nitin H. Vaidya (DSN Workshops, 2021)

Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (L4DC, 2021)

Approximate Byzantine Fault-Tolerance in Distributed Optimization. - Shuo Liu 011; Nirupam Gupta; Nitin H. Vaidya (PODC, 2021)

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? - Rachid Guerraoui; Nirupam Gupta; Rafaël Pinot; Sébastien Rouault; John Stephan (PODC, 2021)

Redundancy in cost functions for Byzantine fault-tolerant federated learning. - Shuo Liu 011; Nirupam Gupta; Nitin H. Vaidya (ResilientFL, 2021)

Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent. - Nirupam Gupta; Nitin H. Vaidya (CoRR, 2021)

Asynchronous Distributed Optimization with Redundancy in Cost Functions. - Shuo Liu 011; Nirupam Gupta; Nitin H. Vaidya (CoRR, 2021)

On Accelerating Distributed Convex Optimizations. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 2021)

Combining Differential Privacy and Byzantine Resilience in Distributed SGD. - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Sébastien Rouault; John Stephan (CoRR, 2021)

Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning. - Shuo Liu 011; Nirupam Gupta; Nitin H. Vaidya (CoRR, 2021)

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (IEEE Control. Syst. Lett., 2021)

Preserving Statistical Privacy in Distributed Optimization. - Nirupam Gupta; Shripad Gade; Nikhil Chopra; Nitin H. Vaidya (IEEE Control. Syst. Lett., 2021)

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2022)

Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants. - Karim Boubouh; Amine Boussetta; Nirupam Gupta; Alexandre Maurer; Rafaël Pinot (SRDS, 2022)

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (Autom., 2022)

Making Byzantine Decentralized Learning Efficient. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

On the Impossible Safety of Large AI Models. - El-Mahdi El-Mhamdi; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (IEEE Trans. Control. Netw. Syst., 2022)

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (AISTATS, 2023)

Impact of Redundancy on Resilience in Distributed Optimization and Learning. - Shuo Liu 011; Nirupam Gupta; Nitin H. Vaidya (ICDCN, 2023)

On the Privacy-Robustness-Utility Trilemma in Distributed Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2023)

Robust Collaborative Learning with Linear Gradient Overhead. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (ICML, 2023)

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk (NeurIPS, 2023)

Distributed Learning with Curious and Adversarial Machines. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (CoRR, 2023)

Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy. - Nirupam Gupta; Thinh T. Doan 001; Nitin H. Vaidya (IEEE Trans. Control. Netw. Syst., 2023)

Robust Machine Learning - Distributed Methods for Safe AI - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (Springer, 2024)

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk; Sasha Voitovych (ICML, 2024)

Revisiting Ensembling in One-Shot Federated Learning. - Youssef Allouah; Akash Balasaheb Dhasade; Rachid Guerraoui; Nirupam Gupta; Anne-Marie Kermarrec; Rafael Pinot; Rafael Pires 001; Rishi Sharma 001 (NeurIPS, 2024)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. - Youssef Allouah; Abdellah El Mrini; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (NeurIPS, 2024)

Brief Announcement: A Case for Byzantine Machine Learning. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (PODC, 2024)

Tackling Byzantine Clients in Federated Learning. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk; Sasha Voitovych (CoRR, 2024)

On the Relevance of Byzantine Robust Optimization Against Data Poisoning. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (CoRR, 2024)

Boosting Robustness by Clipping Gradients in Distributed Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Ahmed Jellouli; Geovani Rizk; John Stephan (CoRR, 2024)

Byzantine Machine Learning: A Primer. - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (ACM Comput. Surv., 2024)

Adaptive Gradient Clipping for Robust Federated Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Ahmed Jellouli; Geovani Rizk; John Stephan (ICLR, 2025)

Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning. - Thomas Boudou; Batiste Le Bars; Nirupam Gupta; Aurélien Bellet (CoRR, 2025)