Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration

arXiv:2312.11489v3 Announce Type: replace-cross
Abstract: Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale …

abstract artificial artificial intelligence arxiv benefit cloud collaboration collaborative combination computing cs.dc cs.lg devices edge expansion federated learning intelligence paradigm privacy scale tasks training type via