  This work focuses on the challenges of non-IID data and stragglers/dropouts
in federated learning. We introduce and explore a privacy-flexible paradigm
that models parts of the clients' local data as non-private, offering a more
versatile and business-oriented perspective on privacy. Within this framework,
we propose a data-driven strategy for mitigating the effects of label
heterogeneity and client straggling on federated learning. Our solution
combines both offline data sharing and approximate gradient coding techniques.
Through numerical simulations using the MNIST dataset, we demonstrate that our
approach enables achieving a deliberate trade-off between privacy and utility,
leading to improved model convergence and accuracy while using an adaptable
portion of non-private data.
