Supporting a Sustainable and Inclusive Urban Agriculture Federation using Dashboarding

2025-08-30

AI Summary

Urban agriculture Living Labs alliances face challenges in cross-regional collaboration due to data silos and privacy constraints, hindering evidence-based decision-making for food security among vulnerable populations. Method: This paper proposes a collaborative dashboard framework integrating federated learning with interactive visualization. It establishes a reusable dashboard development pipeline incorporating standardized multi-source heterogeneous data collection, KPI-driven visual design, and a federated architecture supporting localized model training and global knowledge aggregation. Contribution/Results: We introduce the first privacy-preserving, decision-effective data governance and visualization paradigm tailored for urban agriculture alliances. Validated in the Feed4Food project, the framework demonstrably enhances local situational awareness, fosters coordinated action across regions, and strengthens food system resilience. It provides a scalable, transferable technical pathway for sustainable and inclusive digital governance of urban agriculture.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Reliable access to food is a basic requirement in any sustainable society. However, achieving food security for all is still a challenge, especially for poor populations in urban environments. The project Feed4Food aims to use a federation of Living Labs of urban agriculture in different countries as a way to increase urban food security for vulnerable populations. Since different Living Labs have different characteristics and ways of working, the vision is that the knowledge obtained in individual Living Labs can be leveraged at the federation level through federated learning. With this specific goal in mind, a dashboarding tool is being established. In this work, we present a reusable process for establishing a dashboard that supports local awareness and decision making, as well as federated learning. The focus is on the first steps of this creation, i.e., defining what data to collect (through the creation of Key Performance Indicators) and how to visualize it. We exemplify the proposed process with the Feed4Food project and report on our insights so far.

Problem

• Developing dashboard for urban agriculture federation data integration
• Creating Key Performance Indicators to measure food security
• Enabling federated learning across diverse Living Labs

Innovation

• Federated learning for knowledge sharing
• Dashboard tool for decision support
• Key Performance Indicators for data collection