FedChallenger: Challenge-Response-Based Defence for Federated Learning Against Byzantine Attacks
Author:
Affiliation:
1. Concordia University.
2. ÉTS University of Quebec.
3. Cistech Ltd.
4. Cistel Technology Inc.,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437878.pdf?arnumber=10437878
Reference21 articles.
1. Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors
2. A Novel Data Poisoning Attack in Federated Learning based on Inverted Loss Function
3. Machine learning with adversaries: Byzantine tolerant gradient descent;Blanchard;Advances in neural information processing systems,2017
4. Communication-efficient learning of deep networks from decentralized data;McMahan;Artificial intelligence and statistics. PMLR,2017
5. Byzantine-robust distributed learning: Towards optimal statistical rates;Yin
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