LINDT: Tackling Negative Federated Learning with Local Adaptation
LINDT: Tackling Negative Federated Learning with Local Adaptation
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without disclosing each client's data. However, FL may fail to proceed properly, amid a state that we call negative federated learning (NFL). This paper addresses …