  The success of machine learning (ML) applications relies on vast datasets and
distributed architectures, which, as they grow, present challenges for ML. In
real-world scenarios, where data often contains sensitive information, issues
like data poisoning and hardware failures are common. Ensuring privacy and
robustness is vital for the broad adoption of ML in public life. This paper
examines the costs associated with achieving these objectives in distributed
architectures. We overview the meanings of privacy and robustness in
distributed ML, and clarify how they can be achieved efficiently in isolation.
However, we contend that the integration of these objectives entails a notable
compromise in computational efficiency. We delve into this intricate balance,
exploring the challenges and solutions for privacy, robustness, and
computational efficiency in ML applications.

Machine Learning;Cryptography and Security;Distributed, Parallel, and Cluster Computing