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Towards Verifiable Federated Learning

Towards Verifiable Federated Learning

International Joint Conference on Artificial Intelligence (IJCAI), 2022
15 February 2022
Yanci Zhang
Hanyou Yu
    FedML
ArXiv (abs)PDFHTML

Papers citing "Towards Verifiable Federated Learning"

5 / 5 papers shown
zkUnlearner: A Zero-Knowledge Framework for Verifiable Unlearning with Multi-Granularity and Forgery-Resistance
zkUnlearner: A Zero-Knowledge Framework for Verifiable Unlearning with Multi-Granularity and Forgery-Resistance
Nan Wang
Nan Wu
Xiangyu Hui
Jiafan Wang
Xin Yuan
107
0
0
08 Sep 2025
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Simone Bottoni
Giulio Zizzo
S. Braghin
Alberto Trombetta
AAMLFedML
215
0
0
05 Sep 2025
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Ehsan Hallaji
R. Razavi-Far
R. Razavi-Far
195
0
0
23 Feb 2025
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
Adaptive Coded Federated Learning: Privacy Preservation and Straggler MitigationIEEE Transactions on Communications (IEEE Trans. Commun.), 2024
Chengxi Li
Ming Xiao
Mikael Skoglund
312
6
0
22 Mar 2024
Client-specific Property Inference against Secure Aggregation in
  Federated Learning
Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche
G. Ács
Mario Fritz
FedML
290
13
0
07 Mar 2023
1
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