FLINT: A Platform for Federated Learning Integration
- URL: http://arxiv.org/abs/2302.12862v1
- Date: Fri, 24 Feb 2023 19:38:03 GMT
- Title: FLINT: A Platform for Federated Learning Integration
- Authors: Ewen Wang, Ajay Kannan, Yuefeng Liang, Boyi Chen, Mosharaf Chowdhury
- Abstract summary: Moving from centralized training to cross-device FL for millions or billions of devices presents many risks.
corresponding infrastructure, development costs, and return on investment are difficult to estimate.
We present a device-cloud collaborative FL platform that integrates with an existing machine learning platform.
- Score: 3.0895105898120447
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cross-device federated learning (FL) has been well-studied from algorithmic,
system scalability, and training speed perspectives. Nonetheless, moving from
centralized training to cross-device FL for millions or billions of devices
presents many risks, including performance loss, developer inertia, poor user
experience, and unexpected application failures. In addition, the corresponding
infrastructure, development costs, and return on investment are difficult to
estimate. In this paper, we present a device-cloud collaborative FL platform
that integrates with an existing machine learning platform, providing tools to
measure real-world constraints, assess infrastructure capabilities, evaluate
model training performance, and estimate system resource requirements to
responsibly bring FL into production. We also present a decision workflow that
leverages the FL-integrated platform to comprehensively evaluate the trade-offs
of cross-device FL and share our empirical evaluations of business-critical
machine learning applications that impact hundreds of millions of users.
Related papers
- Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems.
We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
arXiv Detail & Related papers (2023-10-04T20:27:20Z) - FLEdge: Benchmarking Federated Machine Learning Applications in Edge
Computing Systems [77.45213180689952]
We introduce FLEdge, a benchmark targeting FL workloads in edge computing systems.
We systematically study hardware heterogeneity, energy efficiency during training, and the effect of various differential privacy levels on training in FL systems.
We evaluate the impact of client dropouts on state-of-the-art FL strategies with failure rates as high as 50%.
arXiv Detail & Related papers (2023-06-08T13:11:20Z) - An Empirical Study of Federated Learning on IoT-Edge Devices: Resource
Allocation and Heterogeneity [2.055204980188575]
Federated Learning (FL) is a distributed approach in which a single server and multiple clients collaboratively build an ML model without moving data away from clients.
In this study, we systematically conduct extensive experiments on a large network of IoT and edge devices (called IoT-Edge devices) to present FL real-world characteristics.
arXiv Detail & Related papers (2023-05-31T13:16:07Z) - FS-Real: Towards Real-World Cross-Device Federated Learning [60.91678132132229]
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data.
There is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales.
We propose an efficient and scalable prototyping system for real-world cross-device FL, FS-Real.
arXiv Detail & Related papers (2023-03-23T15:37:17Z) - Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation
and Convergence [83.58839320635956]
Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner.
Recent FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets.
This paper addresses how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks.
arXiv Detail & Related papers (2023-03-23T02:42:10Z) - Time-sensitive Learning for Heterogeneous Federated Edge Intelligence [52.83633954857744]
We investigate real-time machine learning in a federated edge intelligence (FEI) system.
FEI systems exhibit heterogenous communication and computational resource distribution.
We propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model.
arXiv Detail & Related papers (2023-01-26T08:13:22Z) - Mobility-Aware Cluster Federated Learning in Hierarchical Wireless
Networks [81.83990083088345]
We develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks.
Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users.
To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm.
arXiv Detail & Related papers (2021-08-20T10:46:58Z) - FedScale: Benchmarking Model and System Performance of Federated
Learning [4.1617240682257925]
FedScale is a set of challenging and realistic benchmark datasets for federated learning (FL) research.
FedScale is open-source with permissive licenses and actively maintained.
arXiv Detail & Related papers (2021-05-24T15:55:27Z) - FLaaS: Federated Learning as a Service [3.128267020893596]
We present Federated Learning as a Service (FL), a system enabling different scenarios of 3rd-party application collaborative model building.
As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices.
We demonstrate FL's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.
arXiv Detail & Related papers (2020-11-18T15:56:22Z) - Estimation of Individual Device Contributions for Incentivizing
Federated Learning [8.426678774799859]
Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of mobile devices.
This paper proposes a computation-and communication-efficient method of estimating a participating device's contribution level.
arXiv Detail & Related papers (2020-09-20T07:03:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.