Karthik Nandakumar

Efficient CNN Building Blocks for Encrypted Data

2021 · arXiv (Cornell University)

Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and …

Multi-Attribute Vision Transformers are Efficient and Robust Learners

2024 · arXiv (Cornell University)

Since their inception, Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) across a wide spectrum of tasks. ViTs exhibit notable characteristics, including global attention, resilience against occlusions, and adaptability …

Redefining Contributions: Shapley-Driven Federated Learning

2024 · arXiv (Cornell University)

Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. While FL finds applications in various domains such as healthcare …