"A Review of Solving Non-IID Data in Federated Learning: Current Status" by Wenhai Lu, Jieren Cheng et al.
Federated learning (FL), as a machine learning framework, has garnered substantial attention from researchers in recent years. FL makes it possible to train a global model through coordination by a central server while ensuring the privacy of data on individual edge devices. However, the data on edge devices that participate in FL training are not independently and identically distributed (IID), resulting in challenges related to heterogeneity data. In this paper, we introduce the challenges generated by non-IID data to FL and provide a detailed classification of non-IID data. Then, we summari...
Source: uow.edu.au
