Federated Learning Architectures: How Data Distribution Shapes Privacy-Preserving AI

Federated learning comes in three main architectural patterns—Horizontal, Vertical, and Federated Transfer Learning—and the right one for your collaboration is determined almost entirely by how your data overlaps with your partners' data in two dimensions: samples and features. Once that architecture is chosen, two complementary privacy mechanisms do the heavy lifting: secure aggregation, which hides each participant's model updates during the aggregation process, and differential privacy, which prevents the resulting global model from leaking any single participant's information.

Introduction to Federated Learning: Collaborative AI with Privacy

The core idea behind federated learning is simple but consequential: instead of pooling raw data in one central location, each participant trains a model locally and shares only the resulting model updates. A central server—or, in decentralized variants, a distributed set of nodes—aggregates those updates into a new global model, which is then sent back to participants. As Palo Alto Networks explains in its technical guide, the process in simplified form runs: clients train locally, send encrypted updates, a central server aggregates them, and a new global model emerges.

Two deployment styles frame most real-world use. Cross-device federated learning involves millions of small devices, such as smartphones, each training briefly when idle. Cross-silo federated learning uses fewer but more capable participants—hospitals or banks—where each participant represents an entire organization. In both cases, the privacy promise rests on the fact that raw data never leaves its origin, a property that researchers at Berkeley's Center for Long-Term Cybersecurity describe as the foundation for collaborative model building across sensitive datasets.

Horizontal Federated Learning: Shared Features, Different Samples

Horizontal Federated Learning (HFL) applies when the feature space of the participating parties' data is the same but the sample space is different—in short, "same features, different samples," as a 2025 academic review in the journal Electronics puts it. Each client holds its own dataset and trains model parameters independently; the centralized aggregation of those parameters enables collaborative learning across parties without sharing raw data.

Think of multiple hospitals that record the same clinical attributes for different patient populations. Their schemas align, but their patients do not overlap. HFL is the natural fit for this scenario, and it is the pattern behind the familiar cross-device case: a global model trained on user data from many mobile devices, where every device contributes unique data instances drawn from an identical feature space.

Vertical Federated Learning: Shared Samples, Different Features

Vertical Federated Learning (VFL) inverts the relationship. Here, datasets share the same sample space but differ in feature space: participants hold different attributes about the same entities. The classic illustration, cited in both the Electronics review and Palo Alto Networks' guide, is a bank and an e-commerce company that serve the same customers—the bank knows their financial behavior, the retailer knows their purchasing habits, and neither holds the other's features.

VFL is therefore the standard pattern for cross-silo collaboration between organizations with overlapping user bases but complementary data types. Rather than merging populations, the architecture merges feature perspectives on a shared population, allowing a jointly trained model to capture relationships neither partner could see alone—without either partner exposing its raw attribute data.

Federated Transfer Learning: Bridging Disparate Data

Federated Transfer Learning (FTL) addresses the most general case of data heterogeneity: participants differ in both sample space and feature space, with little overlap in either dimension. Palo Alto Networks describes FTL as connecting organizations with minimal overlap in users or features, and the Electronics review classifies it as the third category in its distribution-based taxonomy of FL.

This is the hardest and most flexible pattern. Where HFL aligns rows and VFL aligns columns, FTL must bridge datasets that align on neither. Transfer learning techniques allow knowledge learned in one participant's domain to improve modeling in another's, making FTL relevant when organizations want to collaborate but their data landscapes barely intersect. The trade-off is architectural complexity: with little natural alignment to exploit, more of the burden falls on the learning method itself.

Choosing the Right Federated Learning Architecture

The classification below is the practical decision framework: identify how your data overlaps with your partners' in features and samples, and the applicable architecture follows. The Electronics review establishes the taxonomy by distribution pattern of the sample space and feature space; the use-case column reflects the deployment patterns described in Palo Alto Networks' guide.

To use the table, ask two questions in order. First, do you and your partners describe the same kind of records with the same fields? If yes, you are in HFL territory. Second, do you and your partners hold data about the same people or entities? If yes but your fields differ, VFL applies. If the answer to both is no, FTL is the remaining option. The choice is driven primarily by overlap in samples and features—not by preference or vendor positioning.

Secure Aggregation: Protecting Model Updates

Keeping raw data local is only half the privacy story. Model updates themselves can leak information, which is where secure aggregation comes in. Researchers at Berkeley's Center for Long-Term Cybersecurity, describing a blockchain-based federated learning platform, explain that they adapt a secure aggregation protocol "to hide each contributed model from other participants"—the aggregator can compute the combined model without ever seeing any individual contribution in the clear.

The same principle applies in the conventional client-server setting. A 2021 arXiv paper on graph federated learning notes that a single server connected to multiple clients is highly sensitive to communication failures and computational overloads, motivating a private multi-server scheme that uses cryptographic concepts to privatize the federated learning algorithm. Duality Technologies, a vendor of secure data collaboration tooling, adds that the key architectural requirement in regulated sectors is secure orchestration and connectivity between participants and the aggregation layer—including identity controls and encryption—because the main blocker there is permissions and legal exposure, not storage.

Differential Privacy: Guarding Against Inference Attacks

Secure aggregation protects updates in transit and during aggregation, but the aggregated model itself can still reveal a particular client's information. The Berkeley researchers therefore layer a second mechanism on top: differential privacy is applied to protect the global model from revealing any single participant's contribution. The two techniques are complementary rather than redundant—secure aggregation guards the individual updates during aggregation, while differential privacy guards against inference attacks on the final model.

A preprint by Sharathchandra Patil and colleagues at BMS Institute of Technology frames differential privacy and encryption as the core techniques enabling federated learning to preserve user privacy across diverse datasets. Duality's technical blog similarly notes that its platform protects intermediate weights and, in some cases, adds differential privacy—signalling that noise addition is a deliberate, sometimes optional layer whose strength trades off against model accuracy. The Electronics review captures this as a broader privacy-efficiency-accuracy trade-off inherent to the approach.

Implementing Privacy-Preserving Federated Learning

The decision, then, is twofold: select the appropriate federated learning architecture (HFL, VFL, FTL) based on data distribution, and integrate secure aggregation and differential privacy to ensure data confidentiality and model robustness. The architecture follows from your overlap analysis; the privacy stack follows from recognizing that "only sharing model updates" is necessary but not sufficient, since updates and aggregated models can both leak.

Success is measurable. The concrete indicator is successful training of a global model without direct raw data sharing, verifiable through audit logs of aggregated updates and privacy budget monitoring. The audit logs confirm that aggregation operated on protected contributions rather than raw data; the privacy budget—the accounting mechanism differential privacy uses to track cumulative noise expenditure—confirms that the protection is quantified rather than assumed. A collaboration that can produce both artifacts has demonstrably closed the loop between architectural choice and privacy guarantee.

Sources

• What Is Federated Learning? A Guide to Privacy-Preserving AI — Palo Alto Networks
• Privacy-preserving and Decentralized Federated Learning - CLTC
• MDPI
• Which Federated Learning Architecture delivers the best results? — Duality Technologies
• Engrxiv