… We show that this hierarchical federatedlearning (HFL) scheme significantly reduces the … To this end we propose the hierarchical FL framework, where MUs are clustered according to …
C Briggs, Z Fan, P Andras - 2020 international joint conference …, 2020 - ieeexplore.ieee.org
… As such, we propose a federatedlearning with hierarchical clustering (FL+HC) setting. During the FL procedure, a clustering step at communication round n is introduced. At the …
… is the privacy preserving machine learning paradigm known as FederatedLearning (FL), which … To reduce node failures and device dropouts, the HierarchicalFederatedLearning (HFL) …
B Xu, W Xia, W Wen, P Liu, H Zhao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Federatedlearning (FL) is promising in enabling large-scale model training by massive … Fueled by this issue, we consider a hierarchical FL system and formulate a joint problem of edge …
L Liu, J Zhang, S Song… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federatedlearning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by … Hierarchical FL, with a client-edge-cloud aggregation …
FederatedLearning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its …
… In this paper, we have designed a cluster-based federatedlearning mechanism with hierarchical … that our proposed mechanism will provide a valuable solution for federatedlearning. …
… To fill this gap, in this paper, we propose a heterogeneous federatedlearning approach to train machine learning models over heterogeneous EEG data, while preserving the data …
J Feng, L Liu, Q Pei, K Li - IEEE Transactions on Parallel and …, 2021 - ieeexplore.ieee.org
… We propose a hierarchicalfederatedlearning optimization framework over wireless edge networks to study the problem of participant cost minimization in the worst case. …