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- W4328028625 abstract "Privacy protection in the era of big data has attracted attention, and privacy leakage may cause severe user losses. Federated learning (FL) is a distributed machine learning framework that stores data locally in participating nodes. FL is an effective way to protect data privacy under the current background of artificial intelligence. Blockchain is a distributed data structure with the advantages of decentralization, non-tampering, and non-counterfeiting. Traditional FL relies on centralized servers and is prone to the single point of failure, and the problem can be avoided by replacing the centralized server with blockchain technology. However, unauthorized participants may launch poison attacks, and untrusted nodes in the blockchain send faulty aggregation models to users, reducing the model’s availability. Given the above problems, this paper proposes an FL (DS-AVFL) framework with authentication and verification. Blockchain technology is used for dynamic identity management, only updates uploaded by authenticated local devices can be added to the blockchain, and local devices can verify the received global model. Experimental results show that the scheme is feasible and has reasonable computing costs." @default.
- W4328028625 created "2023-03-22" @default.
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- W4328028625 date "2022-12-01" @default.
- W4328028625 modified "2023-09-27" @default.
- W4328028625 title "Federated Learning Scheme with Dual Security of Identity Authentication and Verification" @default.
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- W4328028625 doi "https://doi.org/10.1109/trustcom56396.2022.00099" @default.
- W4328028625 hasPublicationYear "2022" @default.
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