Whitepaper · English · 13 pages
How federated data sharing provides new insights
Why collaboration does not require centralising data—and how data spaces combine governance, trust and distributed infrastructure.
WHITEPAPER
Federated sharing
and data spaces
Control stays with the participants.In this paper
Share and combine value without giving up sovereignty.
The paper introduces federated data sharing as a collaborative model in which organisations work under shared governance while retaining ownership and control of their data. It explains the role of data spaces, their core layers and how distributed processing supports analytics and AI.
- Understand the difference between centralised and federated sharing
- Learn the governance, control and data layers of a data space
- Explore identity, connectors, catalogues, policy and clearing
- See scenarios for distributed analytics and federated learning
Chapter outline
A structured path from strategy to infrastructure.
- 01Unlocking the value of data
Why data becomes more useful when it can be combined—and why control matters.
- 02Federated data sharing
Collaboration across distributed sources without relinquishing ownership.
- 03Data spaces in detail
Governance, control and data planes; participant roles and core services.
- 04Data and processing
Replication, aggregation, distributed analytics and federated learning.
- 05Generic infrastructure
A reusable foundation that can serve multiple domains and data spaces.
Editorial refresh before publication
Keep the argument; update the evidence.
The core sovereignty and federation story remains strong. The migrated edition should replace broad IDSA references with the current Data-sharing Services standards matrix, clarify that shared registries and governance services may be central dependencies, and update product and Yuma attribution.
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Federated sharing and data spaces
PDF · 13 pages · English