Towards Robust Misbehavior Detection in Power Control Systems: A Gradient Quantization Approach-Reference-Cited by-同舟云学术

Towards Robust Misbehavior Detection in Power Control Systems: A Gradient Quantization Approach

Published:2023-11-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 Workshop on Recent Advances in Resilient and Trustworthy ML Systems in Autonomous Networks
language:
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Author:

Husnoo Muhammad Akbar¹^ORCID,Anwar Adnan¹^ORCID,Doss Robin²^ORCID,Hosseinzadeh Nasser¹^ORCID

Affiliation:

1. Deakin University, Geelong, Australia

2. Deakin University, Burwood, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605772.3624005

Reference25 articles.

1. Uttam Adhikari Shengyi Pan Tommy Morris Raymond Borges and Justin Beaver. [n. d.]. https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets Uttam Adhikari Shengyi Pan Tommy Morris Raymond Borges and Justin Beaver. [n. d.]. https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets

2. Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , and Ryan Rogers . 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 ( 2018 ). Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018).

3. Peva Blanchard , El Mahdi El Mhamdi , Rachid Guerraoui, and Julien Stainer. 2017 . Machine Learning with Adversaries : Byzantine Tolerant Gradient Descent (NIPS '17). 118--128. Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent (NIPS'17). 118--128.

4. Understanding Distributed Poisoning Attack in Federated Learning