Reza Nasirigerdeh

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments

2020 · arXiv (Cornell University)

Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients. To address the former challenge, we introduce the federated-centralized …

Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning

2022 · arXiv (Cornell University)

Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has …