FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN

arXiv:2403.16930v1 Announce Type: new
Abstract: Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It enables Artificial Intelligence (AI) at the edge by creating models without sharing the actual data across the network. Existing research works typically focus on generic aspects of non-IID data and heterogeneity in client's system characteristics, but they often neglect the issue of insufficient data for model development, which can arise from …

abstract artificial artificial intelligence arxiv cs.lg data devices distributed edge federated learning gan incomplete data intelligence iot machine machine learning machine learning models mobile mobile devices network nodes privacy research the edge training type