Nir Shlezinger

Joint Privacy Enhancement and Quantization in Federated Learning

2023 · IEEE Transactions on Signal Processing

Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized …

Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels

2023 · arXiv (Cornell University)

Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses …

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

2025 · IEEE Transactions on Signal Processing

Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML models, often associated with their black-box operation and sensitivity to …