Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments

arXiv:2404.14033v1 Announce Type: cross
Abstract: Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, current serverless FL systems still suffer from the presence of …

abstract arxiv as-a-service collaborative computing cs.dc cs.lg data decentralized designing distributed enabling environments federated learning function global machine machine learning paradigm serverless serverless computing service systems technologies training type