  Trustworthy Federated Learning (TFL) typically leverages protection
mechanisms to guarantee privacy. However, protection mechanisms inevitably
introduce utility loss or efficiency reduction while protecting data privacy.
Therefore, protection mechanisms and their parameters should be carefully
chosen to strike an optimal tradeoff between \textit{privacy leakage},
\textit{utility loss}, and \textit{efficiency reduction}. To this end,
federated learning practitioners need tools to measure the three factors and
optimize the tradeoff between them to choose the protection mechanism that is
most appropriate to the application at hand. Motivated by this requirement, we
propose a framework that (1) formulates TFL as a problem of finding a
protection mechanism to optimize the tradeoff between privacy leakage, utility
loss, and efficiency reduction and (2) formally defines bounded measurements of
the three factors. We then propose a meta-learning algorithm to approximate
this optimization problem and find optimal protection parameters for
representative protection mechanisms, including Randomization, Homomorphic
Encryption, Secret Sharing, and Compression. We further design estimation
algorithms to quantify these found optimal protection parameters in a practical
horizontal federated learning setting and provide a theoretical analysis of the
estimation error.
