SEEC: Semantic Vector Federation Across Edge Computing Environments

Abstract

Semantic vector embedding techniques have proven useful in learning semantic representations of data across multiple domains. A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find data most similar to a given sample. State-of-the-art embedding approaches assume all data is available on a single site. However, in many business settings, data is distributed across multiple edge locations and cannot be aggregated due to a variety of constraints. Hence, the applicability of state-of-the-art embedding approaches is limited to freely shared datasets, leaving out applications with sensitive or mission-critical data. This paper addresses this gap by proposing novel unsupervised algorithms called *SEEC* for learning and applying semantic vector embedding in a variety of distributed settings. Specifically, for scenarios where multiple edge locations can engage in joint learning, we adapt the recently proposed federated lear

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