Personalized Federated Learning with Parameter Propagation-Reference-Cited by-同舟云学术

Personalized Federated Learning with Parameter Propagation

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wu Jun¹^ORCID,Bao Wenxuan¹^ORCID,Ainsworth Elizabeth²^ORCID,He Jingrui¹^ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, Champaign, IL, USA

2. USDA-ARS Global Change and Photosynthesis Research Unit & University of Illinois at Urbana-Champaign, Champaign, IL, USA

Funder

National Science Foundation under Award No. IIS-1947203, IIS-2117902, IIS-2137468

Agriculture and Food Research Initiative (AFRI) grant no. 2020-67021-32799/project accession no.1024178 from the USDA National Institute of Food and Agriculture

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599464

Reference48 articles.

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2. Shai Ben-David , John Blitzer , Koby Crammer , Alex Kulesza , Fernando Pereira , and Jennifer Wortman Vaughan . 2010. A theory of learning from different domains. Machine learning 79 ( 2010 ), 151--175. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79 (2010), 151--175.

3. FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery

4. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

5. Yae Jee Cho , Divyansh Jhunjhunwala , Tian Li , Virginia Smith , and Gauri Joshi . 2022. To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. arXiv preprint arXiv:2205.14840 ( 2022 ). Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, and Gauri Joshi. 2022. To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning. arXiv preprint arXiv:2205.14840 (2022).

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