IEEE Federated Machine Learning

Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements, such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. This white paper intends to present an overview of the Federated Machine Learning (FML) technology that can be used as a basis for standards, certifications, laws, policies, and/or product ratings. This white paper targets an educated audience, including lawmakers, corporate and governmental policy makers, manufacturers, engineers, and standard setting bodies. However, this white paper is also easily understood by non?technical managers and policy makers as it provides system developers and manufacturers with an overview of Federated Machine Learning techniques. Finally, one must give credit to the IEEE Federated Machine Learning (P3652.1) working group participants for their tremendous dedication, expertise and thoughtful collaborations, without which the publication of IEEE Std 3652.1?2020 [1] would not have been possible .

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