Towards Demystifying Intra-Function Parallelism in Serverless Computing

Kiener, Michael; Chadha, Mohak; Gerndt, Michael

doi:10.48550/arxiv.2110.12090

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These containers are commonly referred to as function instances [22], [23] 1 https://github.com/andreas-grafberger/fedless and contain the function's language-specific runtime environment. For commercial FaaS platforms, the function instances are launched on the FaaS platforms' traditional Infrastructureas-a-Service Virtual Machines as described in [24], [25]. The runtime is responsible for relaying the invocation events, context information, and responses between the FaaS platform and the function.…”

Section: Introductionmentioning

confidence: 99%

FedLess: Secure and Scalable Federated Learning Using Serverless Computing

Grafberger¹,

Chadha²,

Jindal³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacysensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables remote clients to learn a shared ML model while keeping the data local. However, conventional FL systems face several challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a payper-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resourceefficient.

show abstract

Section: Introductionmentioning

confidence: 99%