Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning

Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users’ private information from the provided data and mist...

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Bibliographic Details
Main Authors: Kaixin Peng, Xiaoying Shen, Le Gao, Baocang Wang, Yichao Lu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Entropy
Subjects:
federated learning
privacy protection
verifiability
homomorphic hash function
Online Access:https://www.mdpi.com/1099-4300/25/8/1125

Internet

https://www.mdpi.com/1099-4300/25/8/1125

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