Open Arsenal

Privacy-Preserving Vision at the Edge.

EdgeLens is an open specification for privacy-preserving, edge-first computer vision systems designed to operate where data sensitivity, regulatory constraints, and performance requirements intersect. It defines a modular architecture that enables organizations to run advanced vision workloads locally on devices, on-premises servers, or isolated edge environments without requiring raw visual data to leave trusted boundaries. At the same time, it supports scalable collaboration through federated learning and verifiable model updates, allowing institutions to benefit from shared improvements without compromising privacy.

At its core, EdgeLens focuses on enabling robust computer vision capabilities such as handwriting recognition, OCR, document understanding, image classification, and structured form extraction while keeping processing local by default. This makes it particularly suited for educational environments, where student work must remain protected, but also extends naturally into enterprise domains such as healthcare imaging, industrial inspection, archival digitization, and secure document processing. Each vision module is designed to be independently deployable and interoperable within a larger system.

A key feature of EdgeLens is its strict privacy and data governance model. It enforces local-first processing, ensures sensitive data never leaves institutional boundaries unless explicitly anonymized, and provides configurable privacy controls for retention, metadata handling, and secure storage. Built-in verification loops and human-in-the-loop workflows allow institutions to validate outputs before any external aggregation occurs, ensuring both accuracy and compliance with internal policies.

EdgeLens also introduces a federated learning framework that enables distributed model improvement across institutions without sharing raw data. Instead, only aggregated updates or anonymized gradients are shared through secure channels. This is paired with a provenance and trust system that tracks model lineage, dataset integrity, and training history, ensuring that every improvement can be verified and audited. Combined with governance and security layers, EdgeLens provides a full lifecycle framework for deploying, improving, and scaling computer vision systems in a privacy-first ecosystem.

This specification is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+) and may be used freely with required attribution under Section 7. A Specification Branding License is available for attribution-free deployments, with fees based on usage, scope, and deployment size.