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FS-REAL: Towards Real-World Cross-Device Federated Learning

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Jingren ZhouAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3829 - 3841

https://doi.org/10.1145/3580305.3599829

Published: 04 August 2023 Publication History

Abstract

Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry. However, 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. Most existing works conduct evaluations with homogeneous devices, which are mismatched with the diversity and variability of heterogeneous devices in real-world scenarios. Moreover, it is challenging to conduct research and development at scale with heterogeneous devices due to limited resources and complex software stacks. These two key factors are important yet underexplored in FL research as they directly impact the FL training dynamics and final performance, making the effectiveness and usability of FL algorithms unclear. To bridge the gap, in this paper, we propose an efficient and scalable prototyping system for real-world cross-device FL, FS-REAL. It supports heterogeneous device runtime, contains parallelism and robustness enhanced FL server, and provides implementations and extensibility for advanced FL utility features such as personalization, communication compression and asynchronous aggregation. To demonstrate the usability and efficiency of FS-REAL, we conduct extensive experiments with various device distributions, quantify and analyze the effect of the heterogeneous device and various scales, and further provide insights and open discussions about real-world FL scenarios. Our system is released to help to pave the way for further real-world FL research and broad applications involving diverse devices and scales.

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The promotional video for KDD'2023 paper, "FS-Real: A Real-world Cross-device Federated Learning Platform". We propose an efficient, scalable, consistent, flexible FL platform to bridge the gap between cross-device FL study and real-world applications.

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Cited By

Ling ZChen DYao LLi YShen YBaeza-Yates RBonchi F(2024)On the Convergence of Zeroth-Order Federated Tuning for Large Language ModelsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671865(1827-1838)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671865
Kuang WQian BLi ZChen DGao DPan XXie YLi YDing BZhou JBaeza-Yates RBonchi F(2024)FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671573(5260-5271)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671573
Chaddad AWu YDesrosiers C(2024)Federated Learning for Healthcare ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2023.332582211:5(7339-7358)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3325822
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Index Terms

FS-REAL: Towards Real-World Cross-Device Federated Learning
1. Information systems
  1. Information systems applications
    1. Data mining
2. Security and privacy
  1. Database and storage security
    1. Information accountability and usage control

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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https://dl.acm.org/doi/10.1145/3637528.3671865
Kuang WQian BLi ZChen DGao DPan XXie YLi YDing BZhou JBaeza-Yates RBonchi F(2024)FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671573(5260-5271)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671573
Chaddad AWu YDesrosiers C(2024)Federated Learning for Healthcare ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2023.332582211:5(7339-7358)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3325822
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Farooq UNaseem SMahmood TLi JRehman ASaba TMustafa L(2024)Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomesThe Journal of Supercomputing10.1007/s11227-024-06087-980:11(16334-16367)Online publication date: 10-Apr-2024
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