Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
CH Hu, Z Chen, EG Larsson - arXiv preprint arXiv:2405.12046, 2024 - arxiv.org
Federated learning (FL) has received significant attention in recent years for its advantages
in efficient training of machine learning models across distributed clients without disclosing …
in efficient training of machine learning models across distributed clients without disclosing …
PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning
Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning
(HFL) enables communication-efficient model training in a widespread area but also incurs …
(HFL) enables communication-efficient model training in a widespread area but also incurs …
Optimizing Resource Allocation in Cloud for Large-Scale Deep Learning Models in Natural Language Processing.
The need for big deep learning models in Natural Language Processing (NLP) keeps rising,
it's important to find the best way to divide up cloud resources so that they can be used …
it's important to find the best way to divide up cloud resources so that they can be used …