Classification
AI Systems Architecture and Data Governance
Overview
Federated Learning is a collaborative machine learning technique where model training occurs across multiple decentralized devices or servers holding local data samples, without exchanging raw data. Each participant computes model updates locally and only shares these updates (such as gradients or weights) with a central server, which aggregates them to improve the global model. This approach enhances privacy by keeping personal or sensitive information on local devices and can reduce data transfer costs. However, federated learning introduces challenges, such as ensuring update integrity, handling heterogeneous data distributions (non-IID data), and managing communication efficiency. Despite not sharing raw data, model updates can still leak information through inference attacks, and aggregation mechanisms must be robust against malicious participants or poisoned updates. Additionally, federated learning can be less efficient when participants have unreliable connectivity or limited computational resources. Thus, while federated learning offers significant privacy and scalability benefits, it is not a panacea for all data governance or security issues.
Governance Context
Federated learning aligns with privacy and data minimization obligations found in frameworks such as the EU's GDPR (General Data Protection Regulation) and the OECD Privacy Guidelines, which encourage limiting the sharing and processing of personal data. Under GDPR, Article 25 (Data Protection by Design and by Default) obligates organizations to implement technical measures that minimize data exposure, a goal federated learning supports by design. Similarly, the NIST Privacy Framework advocates for 'Data Minimization and Retention' (CT.DM-P1), which federated learning operationalizes by keeping raw data local. However, organizations must also implement controls such as secure aggregation protocols (to prevent reconstruction of local data from updates) and robust participant authentication (to mitigate poisoning or Sybil attacks), as recommended in NIST SP 800-53 and ISO/IEC 27001. Additional obligations include maintaining audit trails of model update aggregation and publishing transparency reports about data handling. Compliance also requires transparency about model update handling and potential residual risks from indirect data leakage.
Ethical & Societal Implications
Federated learning advances privacy by design and enables collaborative innovation without centralizing sensitive data. However, it poses new ethical challenges, such as the potential for indirect data leakage through model updates and unequal access to model improvements for participants with limited resources. There are also fairness concerns, as data heterogeneity across participants can bias outcomes. Robust governance is necessary to ensure transparency, equitable participation, and ongoing risk management, particularly in critical domains like healthcare or finance where failures could disproportionately impact vulnerable populations. Additionally, federated learning may inadvertently exclude under-resourced organizations, exacerbating digital divides.
Key Takeaways
Federated learning enables collaborative AI model training without sharing raw data.; It supports privacy and data minimization obligations in regulations like GDPR and NIST.; Security risks include model update leakage, poisoning attacks, and Sybil attacks.; Robust governance requires secure aggregation, participant authentication, and transparency.; Federated learning is not immune to all privacy or fairness risks; residual threats remain.; Sector-specific implementation must address both technical and regulatory requirements.; Audit trails and transparency reports are important for compliance and trust.