As privacy laws tighten globally, organizations face a growing challenge: how to collaborate with data across borders without running afoul of data sovereignty regulations. No matter what industry you’re working in, transferring sensitive data, especially internationally, can introduce legal and ethical risks. In fact, over 130 jurisdictions now have some form of data protection legislation, many with strict localization or cross-border restrictions.
Federated learning offers a promising solution. Instead of moving data to a central location for machine learning, federated learning allows organizations to train models where the data resides. This approach makes it easier to meet global compliance requirements while still gaining insights from sensitive or distributed data.
Federated learning is a method for training machine learning models without aggregating raw data in a single location. Instead, the algorithm is sent to each data source (such as a hospital, bank, or data center), where it trains on local data. Only model updates, never the underlying data, are shared back and aggregated.
This approach contrasts with traditional machine learning, which often relies on collecting all data in a central repository for training. Federated learning keeps data decentralized, which is helpful in scenarios where privacy, security, or regulatory concerns prevent data from being moved or shared.
Data sovereignty means that data is subject to the laws of the country where it is collected, stored, or processed. This has major implications for organizations that operate across borders or handle personal data from citizens in different jurisdictions.
The General Data Protection Regulation is one of the most comprehensive privacy laws globally. It restricts the transfer of personal data outside the EU unless strict safeguards are in place. GDPR also emphasizes:
Cross-border data transfers under GDPR often require standard contractual clauses, adequacy decisions, or binding corporate rules. Even with those in place, centralizing EU personal data in other countries (like the U.S.) can still raise compliance concerns. To date, GDPR enforcement has resulted in more than €5.5 billion in fines, highlighting the risks of non-compliance.
HIPAA governs the use and disclosure of Protected Health Information (PHI) in the U.S. healthcare system. Key provisions include:
In 2023 alone, over 133 million healthcare records were exposed in data breaches, underscoring the risks of centralized systems. Even sharing anonymized or de-identified health data across institutions often requires legal and technical review.
Each of these regulations is different, but they all reflect one growing trend: the need to keep data under local control.
Federated learning supports compliance with global data privacy laws by allowing organizations to analyze and collaborate on data without moving or centralizing it. Instead of transferring sensitive data to a central server, federated learning keeps the data where it originated, whether that’s a hospital, financial institution, or cloud region, while still contributing to a shared machine learning model.
This decentralized approach aligns with several common principles found across major data protection laws, including:
Many privacy regulations around the world prioritize reducing risk, maintaining transparency, and limiting unnecessary data sharing. Federated learning supports these goals by replacing traditional centralized workflows with a decentralized, privacy-preserving method that respects the core tenets of most laws.
Federated learning supports GDPR principles by:
Even when federated learning is used with multiple partners, only model updates are shared. Not user data, not metadata, and not logs. This significantly reduces the risk of non-compliance.
In healthcare environments, federated learning reduces the need for Business Associate Agreements or complex data use agreements because:
By keeping PHI local and minimizing what’s shared, organizations lower the risk of regulatory exposure and avoid the legal hurdles involved with centralized systems.
Federated learning respects data localization mandates by allowing each jurisdiction to maintain full control of its data. Rather than build separate machine learning models for each region, federated learning allows organizations to collaborate on a shared model while complying with local laws.
While federated learning offers significant benefits, organizations should be aware of several practical challenges:
Hospitals and research institutions often want to collaborate on disease prediction, treatment outcomes, or drug discovery. Federated learning enables them to train joint models without sharing sensitive patient data across organizations.
Banks use federated learning for anti-money laundering and fraud detection across branches or institutions, all while preserving client privacy and complying with financial regulations.
Drug companies can collaborate with academic partners or government bodies to analyze patient cohorts for trials, without violating consent agreements or local laws.
At Duality, we help organizations take advantage of federated learning without compromising data privacy or compliance. Duality has integrated NVIDIA FLARE as the core engine for its federated architecture.
The platform combines multiple privacy-enhancing technologies into a unified solution. On top of federated learning, it integrates Trusted Execution Environments (TEEs) or Fully Homomorphic Encryption (FHE) to protect intermediate model weights. Additionally, it offers the option to apply differential privacy by adding noise to the results. Beyond these core capabilities, the platform includes a comprehensive governance framework that allows users to define and enforce policies based on their specific needs.
The adoption of federated learning is no longer hypothetical — Google, Apple, and NVIDIA already use it in production, and the enterprise sector is quickly catching up.
As privacy laws evolve, organizations must find new ways to gain insights from data without violating data sovereignty rules. Federated learning offers a practical solution by keeping data where it belongs, while still allowing meaningful machine learning.
Want to see how this works in practice? Read how Dana-Farber Cancer Institute used Duality’s Secure Collaborative AI to power confidential federated learning in oncology research. By combining secure enclaves and federated learning, Dana-Farber was able to collaborate more freely and efficiently, all while keeping patient data protected.
If your team is looking to collaborate across borders or sectors, it may be time to explore how federated learning can support your privacy goals, without giving up innovation.
