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Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
فدراسیون دانش: یک چارچوب متحد و سلسله مراتبی حفظ حریم خصوصی هوش مصنوعی-2020 With strict protections and regulations of data
privacy and security, conventional machine learning based on
centralized datasets is confronted with significant challenges,
making artificial intelligence (AI) impractical in many missioncritical
and data-sensitive scenarios, such as finance, government,
and health. In the meantime, tremendous datasets are
scattered in isolated silos in various industries, organizations,
different units of an organization, or different branches of an
international organization. These valuable data resources are well
underused. To advance AI theories and applications, we propose
a comprehensive framework (called Knowledge Federation - KF)
to address these challenges by enabling AI while preserving
data privacy and ownership. Beyond the concepts of federated
learning and secure multi-party computation, KF consists of four
levels of federation: (1) information level, low-level statistics and
computation of data, meeting the requirements of simple queries,
searching and simplistic operators; (2) model level, supporting
training, learning, and inference; (3) cognition level, enabling
abstract feature representation at various levels of abstractions
and contexts; (4) knowledge level, fusing knowledge discovery,
representation, and reasoning. We further clarify the relationship
and differentiation between knowledge federation and other
related research areas. We have developed a reference implementation
of KF, called iBond Platform, to offer a productionquality
KF platform to enable industrial applications in finance,
insurance, marketing, and government. The iBond platform will
also help establish the KF community and a comprehensive
ecosystem and usher in a novel paradigm shift towards secure,
privacy-preserving and responsible AI. As far as we know, knowledge
federation is the first hierarchical and unified framework
for secure multi-party computing (statistics, queries, searching,
and low-level operations) and learning (training, representation,
discovery, inference, and reasoning). Index Terms: Knowledge Federation |Knowledge | Federated Learning | Secure Multi-party Computation | Secure Multi-party Learning |
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