FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation-Reference-Cited by-同舟云学术

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FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation

Published:2024-08-24 Issue: Volume:34 Page:3484-3494
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Xia Tong¹^ORCID,Ghosh Abhirup²^ORCID,Qiu Xinchi¹^ORCID,Mascolo Cecilia¹^ORCID

Affiliation:

1. University of Cambridge, Cambridge, United Kingdom

2. University of Birmingham & University of Cambridge, Birmingham, United Kingdom

Funder

the European Research Council (ERC) via the REDIAL project (Grant Agreement ID: 805194)

ERC Project 833296 (EAR)

the UK?s Engineering and Physical Sciences Research Council (EPSRC) with grants EP/M50659X/1 and EP/S001530/1 (the MOA project)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671899

Reference40 articles.

1. Deep Learning with Differential Privacy

2. Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith. 2021. On large-cohort training for federated learning. Advances in neural information processing systems 34 (2021), 20461--20475.

3. A Cluster Separation Measure

4. Inverting Visual Representations with Convolutional Networks

5. Long Pham Duc. 2023. Sound classification . Kaggle. https://www.kaggle.com/ code/longx99/sound-classification/notebook.

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