| BlockFLow: Decentralized, privacy-preserving, and accountable federated machine learning V Mugunthan, R Rahman, L Kagal Blockchain and Applications: 3rd International Congress, 233-242, 2022 | 59* | 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 | 49 | 2020 |
| SMPAI: Secure Multi-Party Computation for Federated Learning V Mugunthan, A Polychroniadou, D Byrd, TH Balch | 49 | 2019 |
| Gradient masked averaging for federated learning I Tenison, SA Sreeramadas, V Mugunthan, E Oyallon, E Belilovsky, I Rish arXiv preprint arXiv:2201.11986, 2022 | 14 | 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 | 12 | 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 | 11 | 2021 |
| 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 | 10 | 2022 |
| 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 | 9 | 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 | 7 | 2023 |
| 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 | 6 | 2017 |
| 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 | 5 | 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 | 5 | 2022 |
| BlockFLow: an accountable and privacy-preserving solution for federated learning. ArXiv V Mugunthan, R Rahman, L Kagal | 5 | 2007 |
| Panel: Privacy Challenges and Opportunities in {LLM-Based} Chatbot Applications S Ghayyur, J Averitt, E Lin, E Wallace, A Deshpande, H Luthi | 2 | 2023 |
| 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 | 1 | 2024 |
| A Practical Approach to Federated Learning V Mugunthan Massachusetts Institute of Technology, 2022 | 1 | 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 | 1 | 2022 |
| Privacy Requirements and Realities of Digital Public Goods G Gopi, RJ Cronk, E Lin, A Frik, E Wallace, M Khan, A Deshpande, ... | | 2023 |
| Prior-Independent Auctions for the Demand Side of Federated Learning A Haupt, V Mugunthan arXiv preprint arXiv:2103.14375, 2021 | | 2021 |
| Utility-Enhancing Flexible Mechanisms for Differential Privacy V Mugunthan, W Xiao, L Kagal International Conference on Privacy in Statistical Databases, 74-90, 2020 | | 2020 |
Lalana KagalMassachusetts Institute of TechnologyVerified email at csail.mit.edu
