  The need for robust, secure and private machine learning is an important goal
for realizing the full potential of the Internet of Things (IoT). Federated
learning has proven to help protect against privacy violations and information
leakage. However, it introduces new risk vectors which make machine learning
models more difficult to defend against adversarial samples. In this study, we
examine the role of differential privacy and self-normalization in mitigating
the risk of adversarial samples specifically in a federated learning
environment. We introduce DiPSeN, a Differentially Private Self-normalizing
Neural Network which combines elements of differential privacy noise with
self-normalizing techniques. Our empirical results on three publicly available
datasets show that DiPSeN successfully improves the adversarial robustness of a
deep learning classifier in a federated learning environment based on several
evaluation metrics.
