1 code implementation • 2 Nov 2021 • Rehmat Ullah, Di wu, Paul Harvey, Peter Kilpatrick, Ivor Spence, Blesson Varghese
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.
1 code implementation • 9 Jul 2021 • Di wu, Rehmat Ullah, Paul Harvey, Peter Kilpatrick, Ivor Spence, Blesson Varghese
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.
no code implementations • 10 Jan 2021 • Andrew Churcher, Rehmat Ullah, Jawad Ahmad, Sadaqat ur Rehman, Fawad Masood, Mandar Gogate, Fehaid Alqahtani, Boubakr Nour, William J. Buchanan
Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms.