Nirupam Gupta

Orcid: 0000-0003-4252-9319

According to our database1, Nirupam Gupta authored at least 47 papers between 2016 and 2024.

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Bibliography

2024
Byzantine Machine Learning: A Primer.
ACM Comput. Surv., July, 2024

Tackling Byzantine Clients in Federated Learning.
CoRR, 2024

Robust Machine Learning - Distributed Methods for Safe AI
Springer, ISBN: 978-981-97-0687-7, 2024

2023
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy.
IEEE Trans. Control. Netw. Syst., December, 2023

Distributed Learning with Curious and Adversarial Machines.
CoRR, 2023

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Robust Collaborative Learning with Linear Gradient Overhead.
Proceedings of the International Conference on Machine Learning, 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning.
Proceedings of the International Conference on Machine Learning, 2023

Impact of Redundancy on Resilience in Distributed Optimization and Learning.
Proceedings of the 24th International Conference on Distributed Computing and Networking, 2023

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations.
IEEE Trans. Control. Netw. Syst., 2022

On the Impossible Safety of Large AI Models.
CoRR, 2022

Making Byzantine Decentralized Learning Efficient.
CoRR, 2022

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem.
Autom., 2022

Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants.
Proceedings of the 41st International Symposium on Reliable Distributed Systems, 2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums.
Proceedings of the International Conference on Machine Learning, 2022

2021
Preserving Statistical Privacy in Distributed Optimization.
IEEE Control. Syst. Lett., 2021

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem.
IEEE Control. Syst. Lett., 2021

Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning.
CoRR, 2021

Combining Differential Privacy and Byzantine Resilience in Distributed SGD.
CoRR, 2021

On Accelerating Distributed Convex Optimizations.
CoRR, 2021

Asynchronous Distributed Optimization with Redundancy in Cost Functions.
CoRR, 2021

Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.
CoRR, 2021

Redundancy in cost functions for Byzantine fault-tolerant federated learning.
Proceedings of the ResilientFL '21: Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning, 2021

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
Proceedings of the PODC '21: ACM Symposium on Principles of Distributed Computing, 2021

Approximate Byzantine Fault-Tolerance in Distributed Optimization.
Proceedings of the PODC '21: ACM Symposium on Principles of Distributed Computing, 2021

Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination.
Proceedings of the 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, 2021

Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy.
Proceedings of the 2021 American Control Conference, 2021

2020
False Data Injection Attacks in Bilateral Teleoperation Systems.
IEEE Trans. Control. Syst. Technol., 2020

Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning.
CoRR, 2020

Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy.
CoRR, 2020

Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE).
CoRR, 2020

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem.
CoRR, 2020

Resilience in Collaborative Optimization: Redundant and Independent Cost Functions.
CoRR, 2020

Fault-Tolerance in Distributed Optimization: The Case of Redundancy.
Proceedings of the PODC '20: ACM Symposium on Principles of Distributed Computing, 2020

Iterative Pre-Conditioning to Expedite the Gradient-Descent Method.
Proceedings of the 2020 American Control Conference, 2020

2019
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning.
CoRR, 2019

Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization.
CoRR, 2019

Byzantine Fault Tolerant Distributed Linear Regression.
CoRR, 2019

Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs.
Proceedings of the 2019 American Control Conference, 2019

Byzantine Fault-Tolerant Parallelized Stochastic Gradient Descent for Linear Regression.
Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018
Information-Theoretic Privacy in Distributed Average Consensus.
CoRR, 2018

Model-Based Encryption: Privacy of States in Networked Control Systems.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017
Robustness of distributive double-integrator consensus to loss of graph connectivity.
Proceedings of the 2017 American Control Conference, 2017

2016
Confidentiality in distributed average information consensus.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

On content modification attacks in bilateral teleoperation systems.
Proceedings of the 2016 American Control Conference, 2016


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