Asset-intensive organisations have invested heavily in enterprise asset management systems and condition monitoring infrastructure, yet most operational data remain underutilised. This presentation challenges the conventional centralised analytics approach and introduces edge federated machine learning as a transformative architecture for physical asset management.

Drawing from real-world implementations across Australian transport, ports, and heavy industry, this session demonstrates how edge computing combined with federated learning solves four critical barriers to intelligent maintenance

Attendees will learn how edge federated ML enables each asset to develop local intelligence whilst contributing to collective= wisdom—all without exposing sensitive operational data. Real-world results demonstrate how distributed asset intelligence transforms maintenance planning, improves resource allocation, and enables truly prescriptive decision-making at the asset level.

Key takeaways include understanding why traditional data collection fails to deliver maintenance value, recognising the architectural advantages of edge federated learning for asset decision-making, and identifying practical first steps for implementing intelligent asset management within current operational environments.

• isolated asset knowledge that prevents fleet-wide learning
• data silos across facilities that block insight sharing
• cloud analytics latency that misses split-second failure indicators
• privacy concerns that lock away valuable operational data