Federated Machine Learning is a decentralized approach to artificial intelligence training where multiple entities collaboratively train a shared model. Individual data sets remain localized, never leaving their owners’ devices or servers. Only model updates or parameters are exchanged, preserving data privacy and reducing reliance on centralized data storage. This method allows for the creation of robust models from diverse data sources without direct data sharing.
Context
The current state of Federated Machine Learning shows increasing application in privacy-sensitive domains like healthcare and finance. A key discussion centers on optimizing communication efficiency and ensuring the security of model updates against adversarial attacks. Critical future developments include expanding its use cases across various industries and integrating it with blockchain technologies for enhanced data provenance and incentive mechanisms.
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