Search Results for author:
Found 3 papers, 3 papers with code
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL.
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth.
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning.
