26 Jun 2024 - das 14h00 às 15h00
Local:
Sala de Seminários do DI
Descrição:
Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train neural network models in a distributed way and participate in federated learning. In a nutshell, SL splits the model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper (edge server, cloud, or other devices). In parallel SL, multiple helpers can process model parts of one or more clients, thus considerably reducing the maximum training time over all clients (makespan).
Ligação:
https://nova-lincs.di.fct.unl.pt/seminars/449
