ASME DRIVN 2026
Overview
ASME DRIVN (pronounced "driven") brings together engineers and researchers working across land- and sea-based transportation systems. The conference spans a broad range of propulsion and mobility technologies - including electrification, internal combustion engines (ICE), hybrid systems, autonomy, safety, and sustainability. ASME DRIVN is a technology-focused forum featuring cutting-edge research papers, high-impact presentations, and engaging panel discussions with influential leaders, along with recognition of innovators shaping the future of transportation and energy systems.
Panel Presentation
Date: Wednesday, Sep 16 · 12:50–2:30 PM
Session: 12-11: Software-Defined Vehicles, Digital Twins, and Connected AI Platforms
Paper Number: 197354
197354 - From Pilot to Production: Operationalizing Federated Learning for Fleet-Scale Edge Ai
Presenter: David Solooki, Forward Deployed Engineer, AI/ML, Rhino Federated Computing
Abstract:
Connected vehicle fleets and industrial transportation assets generate telemetry at a scale that makes centralized model training untenable for bandwidth, cost, and privacy reasons, particularly when data must cross OEM, supplier, or operator boundaries. Federated Learning (FL) is widely cited as a solution, and recent surveys in connected and automated vehicles have mapped its applicability across perception, planning, predictive maintenance, and traffic-flow tasks [1]. Yet most FL efforts in transportation remain in pilot stages and rarely reach production at fleet scale.
While algorithmic progress in FL has accelerated, the primary barriers to fleet-scale deployment are increasingly operational. Production FL across heterogeneous edge fleets requires operational capabilities that off-the-shelf FL frameworks were never designed to provide. We organize these into five requirements any fleet-scale FL platform must satisfy: (1) data security and governance across organizational boundaries, including secure communication and distributed training architectures; (2) orchestration and lifecycle management for large client populations with intermittent connectivity, covering scheduling, rollout, and rollback; (3) observability across training health, client participation, convergence behavior, and communication performance; (4) compute and deployment feasibility on constrained edge hardware under unreliable networks; and (5) algorithmic robustness for non-IID data, partial participation, and personalization.
Open-source FL frameworks have accelerated experimentation but were not designed for the reliability, recoverability, and governance demands of fleet-scale deployment. We contrast the capabilities these frameworks provide against the operational requirements outlined above, and identify the infrastructure and tooling patterns that distinguish a viable production system from a successful pilot.
