TY - GEN
T1 - Towards Federated Learning using FaaS Fabric
AU - Chadha, Mohak
AU - Jindal, Anshul
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - 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).
AB - 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).
KW - FaaS
KW - FaaS platforms
KW - Federated learning
KW - Function-as-a-service
KW - Neural networks
KW - Serverless
UR - http://www.scopus.com/inward/record.url?scp=85099604179&partnerID=8YFLogxK
U2 - 10.1145/3429880.3430100
DO - 10.1145/3429880.3430100
M3 - Conference contribution
AN - SCOPUS:85099604179
T3 - WOSC 2020 - Proceedings of the 2020 6th International Workshop on Serverless Computing, Part of Middleware 2020
SP - 49
EP - 54
BT - WOSC 2020 - Proceedings of the 2020 6th International Workshop on Serverless Computing, Part of Middleware 2020
PB - Association for Computing Machinery, Inc
T2 - 6th International Workshop on Serverless Computing, WOSC 2020 - Part of Middleware 2020
Y2 - 7 December 2020 through 11 December 2020
ER -