Vaikkunth Mugunthan

According to our database, Vaikkunth Mugunthan authored at least 17 papers between 2017 and 2024.

Collaborative distances:

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

2024

PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing.

Blazej Manczak, Eliott Zemour, Eric Lin, Vaikkunth Mugunthan

CoRR, 2024

2023

Gradient Masked Averaging for Federated Learning.

Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky

Trans. Mach. Learn. Res., 2023

Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?

Albert Yu Sun, Eliott Zemour, Arushi Saxena, Udith Vaidyanathan, Eric Lin, Christian Lau, Vaikkunth Mugunthan

CoRR, 2023

Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection.

Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, Vaikkunth Mugunthan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

A Practical Approach to Federated Learning

Vaikkunth Mugunthan

PhD thesis, 2022

Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy.

David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou, Tucker Balch

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks.

Vaikkunth Mugunthan, Eric Lin, Vignesh Gokul, Christian Lau, Lalana Kagal, Steven D. Pieper

Proceedings of the Computer Vision - ECCV 2022, 2022

Overcoming Challenges of Synthetic Data Generation.

Kevin Fang, Vaikkunth Mugunthan, Vayd Ramkumar, Lalana Kagal

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners.

Vaikkunth Mugunthan, Pawan Goyal, Lalana Kagal

CoRR, 2021

Prior-Free Auctions for the Demand Side of Federated Learning.

Andreas Haupt, Vaikkunth Mugunthan

CoRR, 2021

Bias-Free FedGAN.

Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal, Shlomo Dubnov

CoRR, 2021

DPD-InfoGAN: Differentially Private Distributed InfoGAN.

Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal, Shlomo Dubnov

Proceedings of the EuroMLSys@EuroSys 2021, 2021

BlockFLow: Decentralized, Privacy-Preserving, and Accountable Federated Machine Learning.

Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal

Proceedings of the Blockchain and Applications - 3rd International Congress, 2021

2020

BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning.

Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal

CoRR, 2020

Utility-Enhancing Flexible Mechanisms for Differential Privacy.

Vaikkunth Mugunthan, Wanyi Xiao, Lalana Kagal

Proceedings of the Privacy in Statistical Databases, 2020

PrivacyFL: A Simulator for Privacy-Preserving and Secure Federated Learning.

Vaikkunth Mugunthan, Anton Peraire-Bueno, Lalana Kagal

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2017

Shade: A differentially-private wrapper for enterprise big data.

Alexander Heifetz, Vaikkunth Mugunthan, Lalana Kagal

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017