Robust Machine Learning Using Diversity and Blockchain

Machine Learning (ML) algorithms are used in several smart city-based applications. However, ML is vulnerable to adversarial examples that significantly alter its desired output. Therefore, making ML safe and secure is an important research problem to enable smart city-based applications. In this chapter, a mechanism to make ML robust against adversarial examples for predictive analytics-based applications is described. The chapter introduces the concept of diversity where a single predictive analytic task is separately performed using heterogeneous datasets, or ML algorithms, or both. The given diversity components are implemented in distributed platforms using federated learning and edge computing. The diversity components use blockchain to ensure that the data is transferred safely and securely between distributed components such that edge and federated learning devices. The chapter also describes some of the challenges that should be met while adopting diversity mechanism, distributed computation, and blockchain to secure ML.