Governance

What is data governance?

Data governance is the framework of policies, roles, responsibilities, and processes that ensure the responsible, secure, and ethical management of data across its lifecycle. In federated learning, data governance extends beyond traditional practices to address cross-institutional collaboration, where data stays at its source but is still used to train shared models.

FL governance defines who can do what with which data and models, under which conditions, and with which safeguards. It governs both data access and model lifecycle management (e.g. training, sharing, retention, reuse) across all participating organizations, following established data stewardship principles.

Why is data governance important?

Effective governance is foundational to building trust in federated learning environments. It provides clarity and accountability across sites, ensures legal and ethical compliance, and helps align diverse institutional practices. In federated learning, governance enables:

• Secure and ethical collaboration between partners without centralizing data
• Compliance with data protection regulations (e.g. GDPR, HIPAA)
• Clear roles and responsibilities for local and global decision-making
• Transparency in how data and models are handled
• Consistency and quality in local data preparation and model training
• Risk reduction related to data misuse, model drift, or misalignment
• Responsible deployment and monitoring of shared models

Without robust governance, federated learning efforts risk fragmentation, non-compliance, and loss of trust between stakeholders.

What should be considered for data governance?

• To ensure good governance practices that support responsible federated learning and align with FAIR principles, consider:
• Participation Agreements: Formalize agreements that define responsibilities, rights, and liabilities of each partner.
• Decision-Making Structures: Establish clear processes for decisions related to model design, training strategy, access control, and model use.
• Legal & Ethical Compliance: Ensure activities comply with applicable laws (e.g. GDPR), ethics approvals, and institutional data policies.
• Data Sovereignty: Respect the principle that data remains under the control of the data provider at all times.
• Audit Trails & Logging: Maintain detailed logs of data access, training rounds, and model usage for transparency and accountability.
• Access Control: Clearly define who can access, contribute to, or deploy models and infrastructure components.
• Model Lifecycle Governance: Decide when and how models are updated, shared, validated, retired, or reused.
• Training & Awareness: Support all participants with guidance on legal, ethical, and technical aspects of governance in FL.
• Alignment with FAIR: Ensure that metadata, decisions, and governance documents are findable, accessible, interoperable, and reusable. Follow the FAIR Data Principles for comprehensive data management.

Related pages

More information

Links to FAIR Cookbook

Links to DSW

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