most citedFedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

13 citations · 13 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2025

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

Dezhong Yao, Yuexin Shi, Tongtong Liu +1

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The it…

cs.LG2021★ 13 cited

FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

Dezhong Yao, Wanning Pan, Michael J O'Neill +4

One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for d…