Vepakomma, Praneeth

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

Split learning for health: Distributed deep learning without sharing raw patient data

Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 513

Detailed comparison of communication efficiency of split learning and federated learning

Singh, A., Vepakomma, P., Gupta, O., & Raskar, R. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 148

A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities

Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Context-Aware Activity Recognition SystemsHand Gesture Recognition Systems

Cited by 136

Privacy in Deep Learning: A Survey

Mireshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., & Esmaeilzadeh, H. (2020) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 106

A Review of Homomorphic Encryption Libraries for Secure Computation

Sathya, S. S., Vepakomma, P., Raskar, R., Ramachandra, R., & Bhattacharya, S. (2018) · arXiv

Cryptography and Data SecurityCloud Data Security Solutions

Cited by 37