Towards Federated Learning using FaaS Fabric
Mohak Chadha, Anshul Jindal, Michael Gerndt
Abstract
Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a shared model for heterogeneous devices such as resource-constrained edge and cloud makes the efficient management of FL-clients challenging. Furthermore, with the rapid growth of FL-clients, the scaling of FL training process is also difficult.
Topics & Concepts
Computer scienceCloud computingEnhanced Data Rates for GSM EvolutionEdge deviceScalingResource (disambiguation)Process (computing)Artificial neural networkArtificial intelligenceDeep learningFederated learningEdge computingDistributed computingMachine learningDeep neural networksData scienceComputer networkOperating systemGeometryMathematicsPrivacy-Preserving Technologies in DataIoT and Edge/Fog ComputingBlockchain Technology Applications and Security