Demo abstract: Distributed machine learning at resource-limited edge nodes

31 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, methodology): “The works of [8], [11], [12] aimed to optimize the local number of epochs per node with respect to total global iterations in generic resource-constrained edge environments.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Based on the proposed FL scheme, the offloading performance can be improved in terms of better accuracy compared to the heterogeneity-unaware equal task allocation approach [60].”
Adaptive Task Allocation for Mobile Edge Learning (2018, methodology): “Interested readers in the local/global loss function minimization and local parameter aggregations are referred to [11], [12] for further details.”
Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints (2020, background): “The merits of our proposed heterogeneity aware (HA) approach are shown by comparing its performances to the heterogeneity-unaware (HU) equal task allocation approach of [4], [11].”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “Additional challenges should be considered to improve communication efficiencies: 1) Computation resources are heterogeneous and limited at edge devices; 2) Training data at edge devices may be distributed non-uniformly [208]– [210].”