Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning-Reference-Cited by-同舟云学术

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Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning

Published:2020-05 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
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Author:

Yagli Semih,Dytso Alex,Vincent Poor H.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9144330/9153881/09154277.pdf?arnumber=9154277

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Heterogeneous Federated Learning for Non-IID Smartwatch Data Classification;IEEE Internet of Things Journal;2024-09-15

2. Privacy-preserving federated machine learning modeling and predictive control of heterogeneous nonlinear systems;Computers & Chemical Engineering;2024-08

3. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

4. Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15

5. Federated Learning-Driven Intelligent Algorithm Platform for Power Grid Inspection Equipment: Design and Implementation;2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS);2023-11-10

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