Exploiting Sparsity in Over-parameterized Federated Learning over Multiple Access Channels-Reference-Cited by-同舟云学术

Exploiting Sparsity in Over-parameterized Federated Learning over Multiple Access Channels

Published:2024-01-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
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Author:

Kaur Gagandeep¹^ORCID,Prasad Ranjitha¹^ORCID

Affiliation:

1. IIIT Delhi, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632410.3632501

Reference5 articles.

1. An Introduction To Compressive Sampling

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3. Baihe Huang , Xiaoxiao Li , Zhao Song , and Xin Yang . 2021 . Fl-ntk: A neural tangent kernel-based framework for federated learning analysis . In International Conference on Machine Learning. PMLR, 4423–4434 . Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang. 2021. Fl-ntk: A neural tangent kernel-based framework for federated learning analysis. In International Conference on Machine Learning. PMLR, 4423–4434.

4. Arthur Jacot , Franck Gabriel , and Clément Hongler . 2018. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems 31 ( 2018 ). Arthur Jacot, Franck Gabriel, and Clément Hongler. 2018. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems 31 (2018).

5. Over-the-Air Federated Learning From Heterogeneous Data