Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
This work addresses privacy and efficiency issues in federated learning for businesses dealing with heterogeneous data, though it is incremental as it builds on existing gradient coding and data sharing methods.
The paper tackled the challenges of non-IID data and stragglers in federated learning by introducing a privacy-flexible paradigm that models some client data as non-private, combining offline data sharing and approximate gradient coding to achieve a trade-off between privacy and utility, resulting in improved model convergence and accuracy on the MNIST dataset.
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.