Vaikkunth Mugunthan
Vaikkunth Mugunthan
PhD at MIT ; CEO/Co-Founder @ DynamoFL
Verified email at
Cited by
Cited by
BlockFLow: Decentralized, privacy-preserving, and accountable federated machine learning
V Mugunthan, R Rahman, L Kagal
Blockchain and Applications: 3rd International Congress, 233-242, 2022
PrivacyFL: A simulator for privacy-preserving and secure federated learning
V Mugunthan, A Peraire-Bueno, L Kagal
29th ACM International Conference on Information & Knowledge Management, 2020
SMPAI: Secure Multi-Party Computation for Federated Learning
V Mugunthan, A Polychroniadou, D Byrd, TH Balch
Gradient masked averaging for federated learning
I Tenison, SA Sreeramadas, V Mugunthan, E Oyallon, I Rish, E Belilovsky
arXiv preprint arXiv:2201.11986, 2022
Fedltn: Federated learning for sparse and personalized lottery ticket networks
V Mugunthan, E Lin, V Gokul, C Lau, L Kagal, S Pieper
European Conference on Computer Vision, 69-85, 2022
Bias-free fedgan: A federated approach to generate bias-free datasets
V Mugunthan, V Gokul, L Kagal, S Dubnov
arXiv preprint arXiv:2103.09876, 2021
Multi-vfl: A vertical federated learning system for multiple data and label owners
V Mugunthan, P Goyal, L Kagal
arXiv preprint arXiv:2106.05468, 2021
Dpd-infogan: Differentially private distributed infogan
V Mugunthan, V Gokul, L Kagal, S Dubnov
Proceedings of the 1st Workshop on Machine Learning and Systems, 1-6, 2021
Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?
AY Sun, E Zemour, A Saxena, U Vaidyanathan, E Lin, C Lau, ...
arXiv preprint arXiv:2307.16382, 2023
Overcoming challenges of synthetic data generation
K Fang, V Mugunthan, V Ramkumar, L Kagal
2022 IEEE International Conference on Big Data (Big Data), 262-270, 2022
Collusion resistant federated learning with oblivious distributed differential privacy
D Byrd, V Mugunthan, A Polychroniadou, T Balch
Proceedings of the Third ACM International Conference on AI in Finance, 114-122, 2022
Shade: A differentially-private wrapper for enterprise big data
A Heifetz, V Mugunthan, L Kagal
2017 IEEE International Conference on Big Data (Big Data), 1033-1042, 2017
BlockFLow: an accountable and privacy-preserving solution for federated learning. ArXiv
V Mugunthan, R Rahman, L Kagal
Panel: Privacy Challenges and Opportunities in {LLM-Based} Chatbot Applications
S Ghayyur, J Averitt, E Lin, E Wallace, A Deshpande, H Luthi
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
T Kim, E Lin, J Lee, C Lau, V Mugunthan
Advances in Neural Information Processing Systems 36, 2024
A Practical Approach to Federated Learning
V Mugunthan
Massachusetts Institute of Technology, 2022
Gradient Masking for Generalization in Heterogenous Federated Learning
I Tenison, SA Sreeramadas, V Mugunthan, E Belilovsky, I Rish
arXiv preprint Arxiv:2201.11986, 2022
Privacy Requirements and Realities of Digital Public Goods
G Gopi, RJ Cronk, E Lin, A Frik, E Wallace, M Khan, A Deshpande, ...
Prior-Independent Auctions for the Demand Side of Federated Learning
A Haupt, V Mugunthan
arXiv preprint arXiv:2103.14375, 2021
Utility-Enhancing Flexible Mechanisms for Differential Privacy
V Mugunthan, W Xiao, L Kagal
International Conference on Privacy in Statistical Databases, 74-90, 2020
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