FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks-Reference-Cited by-同舟云学术

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Published:2022-08-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 51st International Conference on Parallel Processing
language:
Short-container-title:

Author:

Jang Jaehee¹,Ha Heoneok¹,Jung Dahuin¹,Yoon Sungroh²

Affiliation:

1. Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea

2. Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea and AIIS, ASRI, and INMC, Seoul National University, Republic of Korea

Funder

National Research Foundation of Korea, Ministry of Science and ICT

BK21 FOUR program of the Education and Research Program for Future ICT Pioneers

LG Innotek

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3545008.3545073

Reference29 articles.

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2. Exploring Simple Siamese Representation Learning

3. Gregory Cohen , Saeed Afshar , Jonathan Tapson , and Andre Van Schaik . 2017 . EMNIST: Extending MNIST to Handwritten Letters. In 2017 international joint conference on neural networks (IJCNN) . IEEE , 2921–2926. Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik. 2017. EMNIST: Extending MNIST to Handwritten Letters. In 2017 international joint conference on neural networks (IJCNN). IEEE, 2921–2926.

4. Kedar Dhamdhere Mukund Sundararajan and Qiqi Yan. 2018. How Important is a Neuron?arXiv preprint arXiv:1805.12233(2018). Kedar Dhamdhere Mukund Sundararajan and Qiqi Yan. 2018. How Important is a Neuron?arXiv preprint arXiv:1805.12233(2018).

5. Enmao Diao , Jie Ding , and Vahid Tarokh . 2021 . HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In International Conference on Learning Representations (ICLR). Enmao Diao, Jie Ding, and Vahid Tarokh. 2021. HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In International Conference on Learning Representations (ICLR).

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