Towards Federated Learning using FaaS Fabric


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. In this paper, we propose a possible solution to these challenges: federated learning over a combination of connected Function-as-a-Service platforms, i.e., FaaS fabric offering a seamless way of extending FL to heterogeneous devices. Towards this, we present FedKeeper, a tool for efficiently managing FL over FaaS fabric. We demonstrate the functionality of FedKeeper by using three FaaS platforms through an image classification task with a varying number of devices/clients, different stochastic optimizers, and local computations (local epochs)

Proceedings of the 2020 Sixth International Workshop on Serverless Computing

My research interests include cloud computing, specifically focussing on serverless computing for heterogeneous systems, edge computing, and AIOps.