4 papers
On Harnessing Idle Compute at the Edge for Foundation Model Training
Leyang Xue, Meghana Madhyastha, Myungjin Lee +3
The foundation-model ecosystem remains highly centralized because training requires immense compute resources and is therefore largely limited to large cloud operators. Edge-assist…
Towards Decentralized and Sustainable Foundation Model Training with the Edge
Leyang Xue, Meghana Madhyastha, Randal Burns +2
Foundation models are at the forefront of AI research, appealing for their ability to learn from vast datasets and cater to diverse tasks. Yet, their significant computational dema…
Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
Faisal Ahmed, Myungjin Lee, Shao-Yu Lien +4
The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, an…
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
Faisal Ahmed, Myungjin Lee, Suresh Subramaniam +3
Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for…