How does zero trust address security in federated learning environments where data is shared but not centralized?
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How does zero trust address security in federated learning environments where data is shared but not centralized?
Zero trust addresses security in federated learning environments by implementing strict authentication and authorization mechanisms to ensure that access to data and models is granted based on specific policies and not on assumed trust. In a federated learning setup where data is shared but not centralized, zero trust principles focus on verifying the identity of all participants, securing data transmission through encryption, and continuously monitoring activities to detect any anomalies or unauthorized access attempts. This approach helps in maintaining data privacy, confidentiality, and integrity while enabling collaborative machine learning across multiple entities.