As machine learning and artificial intelligence (ML/AI) are becoming more popular and advanced, there is a wish to turn sensitive data into valuable information through ML/AI techniques revealing only data that is allowed by the parties concerned, or without revealing any information about the data to third parties. Collaborative ML approaches like federated learning (FL) help tackle these needs and concerns, bringing a way to use sensitive data without disclosing critically sensitive features of that data. In this paper, we provide a detailed analysis of state of the art for collaborative ML approaches from a privacy perspective.
Full abstract in IEEEXplore DOI: 10.1109/ACCESS.2022.3204037
Published in IEEE Access volume 10, Sep 5, 2022.
Open Access under a Creative Commons License.