Federated learning (FL) and collaborative AI (CAI) offer new ways to make city logistics more efficient, sustainable, and resilient while protecting sensitive data. In traditional logistics, sharing operational data between companies or with cities is often difficult due to competition and privacy concerns. FL solves this by allowing each company to train AI models locally on its own data, sharing only model updates for aggregation into a stronger global model.
Applied to city logistics, FL can improve demand forecasting across retailers, parcel carriers, and horeca suppliers, enabling smarter consolidation and fewer empty trips. It can also support dynamic routing and kerbside management by combining insights on delivery slots and vehicle flows without exposing proprietary data. In addition, FL can underpin asset sharing by helping optimise the use of micro-hubs, charging stations, and vehicles, while CAI ensures human decision-makers remain in control.
For sustainability and compliance, FL allows operators and municipalities to build emissions models without disclosing raw trip data. Combined with CAI, disruptions such as strikes, roadworks, or extreme weather can be detected earlier, and responses coordinated more effectively. Together, FL and CAI provide a trusted framework for collaboration, enhancing efficiency, safety, and innovation in city logistics.
Future research should explore Adaptive Federated Learning (FL) in broader supply chain information-sharing settings, including multi-tier applications like demand prediction. Further work is also needed on digitalisation options to support Adaptive FL in practice. To evaluate its impact on decision-making efficiency, extensive simulations of dynamic, real-world environments with feedback loops are recommended. Such simulations would provide a more robust assessment of the method’s applicability and contribute to understanding decision-making in complex, evolving contexts.
Why this matters in city logistics
- Federated learning solves the trust and data-sharing problem (no raw data leaves the company).
- Collaborative AI combines human expertise (from drivers, planners, and policymakers) with machine intelligence (speed, optimization, scenario modeling).
- Together, they support cleaner, safer, and more reliable urban deliveries while respecting competition and privacy constraints.
Here’s a step-by-step guide to organising federated learning (FL) in practice:
1. Define the use case and objectives
- Identify a concrete business problem (e.g., demand forecasting, predictive maintenance, routing).
- Agree on success metrics (accuracy, cost savings, CO₂ reduction, service levels).
2. Form the consortium and governance model
- Roles: coordinator/aggregator, participating organisations (data owners), and possibly an auditor.
- Set rules for ownership of the global model, intellectual property, and data usage.
- Establish decision-making and dispute resolution processes.
3. Align data structures without sharing raw data
- Harmonise feature definitions, time resolution, and units across participants.
- Define data quality rules and missing-value policies.
- Ensure participants can validate their local data against agreed standards.
4. Build in privacy and security from the start
- Data stays local; only model updates are shared.
- Use secure aggregation; consider differential privacy or encryption for sensitive domains.
- Enforce authentication, logging, and monitoring of all model updates.
5. Choose technical architecture and tools
- Decide on topology: central aggregator (most common) or decentralised peer-to-peer.
- Select a framework: Flower, TensorFlow Federated, PySyft, or enterprise platforms.
- Set up infrastructure for versioning, scheduling rounds, and containerised deployment.
6. Design and train the model
- Start with a baseline model to benchmark improvements.
- Use federated averaging (FedAvg) or more advanced optimisers (FedProx, FedOpt).
- Mitigate non-IID data issues with client sampling or weighting strategies.
7. Evaluate and validate
- Each participant evaluates the model locally on test sets.
- Combine results to measure overall and per-site performance.
- Run pilot A/B tests before scaling to production.
8. Operationalise with MLOps
- Automate updates, monitoring, and re-training cycles.
- Detect model drift and handle rollback if quality drops.
- Keep audit trails for compliance and reproducibility.
9. Define incentives and benefit-sharing
- Ensure all parties gain from participation (better accuracy, efficiency, or cost reduction).
- Consider contribution-based weighting for access to or use of the global model.
10. Ensure legal and compliance alignment
- Address GDPR/data protection, contracts, and sector-specific regulations.
- Formalise agreements on exit, retention, and liability.
Walther Ploos van Amstel.
Also check out: Graph-Based Digital Twins: Building Resilient, Efficient, and Sustainable Supply Chains