FLGo: A Fully Customizable Federated Learning Platform

• URL: http://arxiv.org/abs/2306.12079v1
• Date: Wed, 21 Jun 2023 07:55:29 GMT
• Title: FLGo: A Fully Customizable Federated Learning Platform
• Authors: Zheng Wang, Xiaoliang Fan, Zhaopeng Peng, Xueheng Li, Ziqi Yang, Mingkuan Feng, Zhicheng Yang, Xiao Liu, and Cheng Wang
• Abstract summary: We propose a novel lightweight Federated learning platform called FLGo. Our platform offers 40+ benchmarks, 20+ algorithms, and 2 system simulators as out-of-the-box plugins. We also develop a range of experimental tools, including parallel acceleration, experiment tracker and parameters auto-tuning.
• Score: 23.09038374160798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the performance of FL under realistic conditions. However, the process of customizing simulations to accommodate application-specific settings, data heterogeneity, and system heterogeneity typically remains unnecessarily complicated. This creates significant hurdles for traditional ML researchers in exploring the usage of FL, while also compromising the shareability of codes across FL frameworks. To address this issue, we propose a novel lightweight FL platform called FLGo, to facilitate cross-application FL studies with a high degree of shareability. Our platform offers 40+ benchmarks, 20+ algorithms, and 2 system simulators as out-of-the-box plugins. We also provide user-friendly APIs for quickly customizing new plugins that can be readily shared and reused for improved reproducibility. Finally, we develop a range of experimental tools, including parallel acceleration, experiment tracker and analyzer, and parameters auto-tuning. FLGo is maintained at \url{flgo-xmu.github.io}.

Related papers

• FedModule: A Modular Federated Learning Framework [5.872098693249397]
  Federated learning (FL) has been widely adopted across various applications, such as healthcare, finance, and smart cities. This paper introduces FedModule, a flexible and FL experimental framework. FedModule adheres to the "one code, all scenarios" principle and employs a modular design that breaks the FL process into individual components.
  arXiv  Detail & Related papers  (2024-09-07T15:03:12Z)
• pfl-research: simulation framework for accelerating research in Private Federated Learning [6.421821657238535]
  pfl-research is a fast, modular, and easy-to-use Python framework for simulating Federated learning (FL) It supports setups, PyTorch, and non-neural network models, and is tightly integrated with state-of-the-art algorithms. We release a suite of benchmarks that evaluates an algorithm's overall performance on a diverse set of realistic scenarios.
  arXiv  Detail & Related papers  (2024-04-09T16:23:01Z)
• 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)
• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings [51.09574369310246]
  Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models. We propose a novel cross-silo dataset suite focused on healthcare, FLamby, to bridge the gap between theory and practice of cross-silo FL. Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research.
  arXiv  Detail & Related papers  (2022-10-10T12:17:30Z)
• UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks [53.20176108643942]
  We present UniFed, the first unified platform for standardizing open-source Federated Learning (FL) frameworks. UniFed streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks. We evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance.
  arXiv  Detail & Related papers  (2022-07-21T05:03:04Z)
• FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing [63.87056362712879]
  We propose a novel and comprehensive federated learning platform, named FederatedScope, which is based on a message-oriented framework. Compared to the procedural framework, the proposed message-oriented framework is more flexible to express heterogeneous message exchange. We conduct a series of experiments on the provided easy-to-use and comprehensive FL benchmarks to validate the correctness and efficiency of FederatedScope.
  arXiv  Detail & Related papers  (2022-04-11T11:24:21Z)
• FL_PyTorch: optimization research simulator for federated learning [1.6114012813668934]
  Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model. FL_PyTorch is a suite of open-source software written in python that builds on top of one the most popular research Deep Learning (DL) framework PyTorch.
  arXiv  Detail & Related papers  (2022-02-07T12:18:28Z)
• EasyFL: A Low-code Federated Learning Platform For Dummies [21.984721627569783]
  We propose the first low-code Federated Learning (FL) platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. Our implementations show that EasyFL requires only three lines of code to build a vanilla FL application, at least 10x lesser than other platforms.
  arXiv  Detail & Related papers  (2021-05-17T04:15:55Z)
• Flower: A Friendly Federated Learning Research Framework [18.54638343801354]
  Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model. We present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments.
  arXiv  Detail & Related papers  (2020-07-28T17:59:07Z)
• FedML: A Research Library and Benchmark for Federated Machine Learning [55.09054608875831]
  Federated learning (FL) is a rapidly growing research field in machine learning. Existing FL libraries cannot adequately support diverse algorithmic development. We introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison.
  arXiv  Detail & Related papers  (2020-07-27T13:02:08Z)

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.