Approximate Gradient Coding for Privacy-Flexible Federated Learning with
Non-IID Data
Approximate Gradient Coding for Privacy-Flexible Federated Learning with
Non-IID Data
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 …