What Are the Types of Federated Learning

As data privacy and security concerns grow in today’s highly connected world, Federated Learning (FL) has emerged as a transformative approach for training machine learning models in a decentralized and privacy-preserving manner. Traditional machine learning methods often require large amounts of centralized data for training, but this approach presents risks when dealing with sensitive information, such as personal health records or financial transactions.

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Federated Learning solves this by allowing models to be trained across multiple devices or organizations, without ever needing to share the raw data. Instead, individual devices train models locally and only share model updates (e.g., weights or gradients) with a central server, ensuring that the raw data remains decentralized.

While Federated Learning is a general concept, it can be applied in different ways depending on the data distribution, the network of devices involved, and the overall learning environment. In this comprehensive blog post, we will dive deep into the types of Federated Learning, their specific use cases, and how they cater to various data privacy and infrastructure challenges. We will also address some frequently asked questions (FAQs) to help clarify the different approaches to Federated Learning and when to use each type.

What Is Federated Learning?

Federated Learning is a machine learning technique where a model is trained across multiple decentralized devices or servers that each hold local data samples, without transferring the data itself to a central server. The devices, often referred to as clients, train a shared model by sending only model updates, such as gradients, to a central aggregator, which combines the updates to improve the global model. This process preserves privacy since the raw data never leaves the devices.

Federated Learning was first introduced by Google in 2017 as a way to improve services like predictive text, where sensitive data like user typing information is kept on the user’s device while still allowing for model training. Since then, the concept has gained significant traction in various industries such as healthcare, finance, and the Internet of Things (IoT).

Types of Federated Learning

Federated Learning is generally categorized into three main types based on the distribution of data across clients and the structure of the system. These types are:

Horizontal Federated Learning (HFL)
Vertical Federated Learning (VFL)
Federated Transfer Learning (FTL)

Each type of Federated Learning is tailored to specific scenarios based on how data is distributed across devices or organizations. Below, we’ll explore each type in detail.

1. Horizontal Federated Learning (HFL)

Horizontal Federated Learning (HFL) is the most commonly known and widely used form of Federated Learning. In this approach, the dataset across the participating clients (devices or organizations) shares the same features but has different samples. In other words, each device holds data that represents different users or individuals, but the features (or variables) collected are the same.

How Horizontal Federated Learning Works:

In HFL, devices such as smartphones or IoT devices have datasets with the same structure (same feature space) but different data points (i.e., each device contains data on different users). The shared model is trained on these decentralized datasets without aggregating the raw data in a central location. Only the model updates are sent to a central server for aggregation.

For example, in a predictive text application, all users have the same input features (keyboard data, language, etc.), but each device holds unique typing patterns for individual users.

Use Cases of Horizontal Federated Learning:

Smartphone Applications: Predictive text suggestions, voice recognition, and keyboard prediction (e.g., Google’s Gboard) are applications where Horizontal Federated Learning is used to improve user experience without sending user data to the cloud.
IoT Devices: In the Internet of Things, edge devices (such as smart home appliances) can share model updates rather than raw data, ensuring privacy while building a shared model for performance improvements.
Healthcare: Medical facilities in different geographical regions with the same data structure (e.g., patient records) can collaborate using HFL without needing to centralize patient data.

Advantages of Horizontal Federated Learning:

Data Privacy: Since raw data never leaves the device, HFL ensures data privacy and meets compliance standards like GDPR.
Efficiency: HFL leverages local computation, which reduces the need for transferring massive amounts of data over the network.

Limitations of Horizontal Federated Learning:

Network Dependency: Clients need to communicate model updates with the central server, which requires stable and frequent network access.
Non-IID Data: Often, the data on devices is non-independent and identically distributed (non-IID), which can make model convergence slower and harder to achieve.

2. Vertical Federated Learning (VFL)

Vertical Federated Learning (VFL) is used when different clients or organizations hold data with different features but for the same sample set or users. This typically happens when multiple entities (e.g., organizations, institutions) collaborate to build a model while each has different types of information about the same user or entity.

How Vertical Federated Learning Works:

In VFL, multiple entities have complementary datasets. For instance, a bank may have financial data on users, while an e-commerce company might have purchasing habits for the same users. Both entities share the same users but possess different sets of features. Vertical Federated Learning allows these organizations to collaborate and build a machine learning model that leverages both datasets while keeping the raw data private.

For instance, VFL is typically used when two businesses wish to build a more comprehensive user profile by combining different feature sets (e.g., financial history + purchase history), but neither wants to expose its raw data to the other. Model updates are exchanged instead of raw data, preserving user privacy and security.

Use Cases of Vertical Federated Learning:

Cross-Industry Collaboration: VFL is useful for situations where companies from different industries want to share insights without sharing actual data. For example, a collaboration between a bank and an insurance company could improve fraud detection algorithms.
Healthcare and Genomics: Medical institutions and pharmaceutical companies may share relevant features to build predictive models (e.g., linking genetic profiles with health outcomes) without exchanging sensitive raw data.
Finance: Banks and credit scoring agencies can collaborate to build more accurate credit scoring models by sharing feature sets (e.g., transactional data and credit history) without transferring sensitive data between them.

Advantages of Vertical Federated Learning:

Feature Collaboration: VFL allows multiple entities to collaborate on shared samples without compromising the privacy of unique feature sets.
Improved Accuracy: Models trained using VFL can leverage a wider range of features, potentially improving the predictive power of the model.

Limitations of Vertical Federated Learning:

Complexity: Coordinating training across multiple parties with different feature sets and ensuring data privacy adds complexity to the system.
Communication Overhead: There can be higher communication costs when multiple organizations are involved, as more coordination is needed to share model updates and aggregate results.

3. Federated Transfer Learning (FTL)

Federated Transfer Learning (FTL) combines the principles of Federated Learning with Transfer Learning, making it ideal for situations where the clients have neither overlapping data samples nor overlapping features. This type of Federated Learning is especially useful when different parties have datasets that are neither horizontally nor vertically aligned, but still wish to collaborate to build a global model.

How Federated Transfer Learning Works:

In FTL, knowledge from a pre-trained model is transferred between clients that have different feature spaces and user sets. FTL can be used to transfer learning from one domain or dataset to another, even when the clients are working with different features and user groups. This is particularly valuable in situations where one client has more comprehensive data but wants to enable collaboration with another entity that has a smaller or more fragmented dataset.

For example, a company in the United States may have developed a customer satisfaction model based on a large dataset. Through FTL, this knowledge can be transferred to a partner company in Europe that has a similar business model but fewer data points, enabling the European company to benefit from the insights without accessing the raw data.

Use Cases of Federated Transfer Learning:

International Collaboration: Organizations operating in different geographical regions with different datasets can use FTL to benefit from each other’s models without needing direct data access.
Small Data Scenarios: Companies with limited data but shared objectives (e.g., small retailers) can use FTL to collaborate with larger companies and build more accurate models.
Cross-Industry Learning: FTL can be applied in scenarios where companies from different industries need to share knowledge across models, even though their data doesn’t fully align.

Advantages of Federated Transfer Learning:

Data Generalization: FTL allows organizations to build models that generalize across different datasets, even when there is no overlap in samples or features.
Small Data Collaboration: FTL helps smaller companies or organizations with limited datasets collaborate with larger entities to improve model accuracy.

Limitations of Federated Transfer Learning:

Model Complexity: The process of transferring knowledge between domains with different features and samples can be more complex and computationally expensive.
Performance Challenges: Since data features and samples are different, ensuring that the transferred knowledge is relevant and beneficial can be difficult.

Advantages of Federated Learning (Across All Types)

Regardless of the type of Federated Learning, the general advantages of the framework include:

Data Privacy: Federated Learning enables data privacy because the data never leaves the local devices. Only model updates are shared, which reduces the risk of data breaches.
Regulatory Compliance: Federated Learning helps organizations comply with privacy regulations like GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) since data remains localized.
Scalability: Since training is distributed across many devices or organizations, Federated Learning can scale to handle extremely large numbers of participants without needing a centralized data pool.
Reduced Latency: With computations happening locally on the edge devices, Federated Learning can reduce the latency compared to traditional centralized training, especially for time-sensitive applications.

Comparison Table: Types of Federated Learning

Feature	Horizontal Federated Learning (HFL)	Vertical Federated Learning (VFL)	Federated Transfer Learning (FTL)
Data Distribution	Same features, different samples	Same samples, different features	Different features, different samples
Typical Client Setup	Individual devices (smartphones, IoT devices)	Multiple organizations (e.g., bank and retailer)	Organizations with distinct data sources
Use Cases	Mobile applications, IoT devices, smart homes	Cross-industry collaboration (finance, healthcare)	International or cross-industry knowledge sharing
Communication Requirements	Moderate communication between clients and central server	Higher communication overhead between entities	Variable, depending on the scale of transfer
Main Advantage	Preserves privacy while sharing the same feature space	Leverages different feature sets without sharing raw data	Enables collaboration between organizations with small data
Challenges	Handling non-IID data distribution across devices	Complex coordination between multiple organizations	Transfer learning relevance between different datasets

Frequently Asked Questions (FAQs)

1. Can different types of Federated Learning be combined?

Yes, it’s possible to combine different types of Federated Learning in certain use cases. For example, Horizontal Federated Learning and Vertical Federated Learning can be combined when collaborating across entities that share both similar users and feature spaces, but for different data subsets.

2. Which type of Federated Learning is best for privacy?

All types of Federated Learning offer privacy advantages, as raw data is never shared. However, Horizontal Federated Learning is particularly popular for privacy-sensitive applications like smartphone-based text prediction, where personal data remains on the device.

3. Does Federated Learning compromise model accuracy?

Federated Learning may encounter challenges like non-IID data or device heterogeneity, which can affect model convergence. However, through techniques like Federated Averaging and optimization algorithms, these issues can often be mitigated, leading to competitive model accuracy compared to traditional centralized learning.

4. What are the main challenges of Federated Learning?

Some of the key challenges include:

Communication Overhead: Federated Learning involves frequent communication between clients and the server, which can cause network bottlenecks.
Non-IID Data: Data on different devices may not follow the same distribution, making model convergence more difficult.
Device Heterogeneity: Devices participating in Federated Learning may vary greatly in computational power, leading to uneven performance.

5. What industries benefit most from Federated Learning?

Industries that prioritize data privacy and decentralized data, such as healthcare, finance, mobile applications, and the Internet of Things (IoT), benefit the most from Federated Learning.

6. How does Federated Learning differ from traditional machine learning?

Unlike traditional machine learning, which requires all data to be aggregated in a central server for training, Federated Learning trains models on local data at each client, ensuring that raw data never leaves the device.

7. Is Federated Learning secure?

Federated Learning offers greater privacy than centralized learning, but it can still be vulnerable to attacks, such as model poisoning or inference attacks. Techniques like differential privacy and secure aggregation are often applied to further enhance security.

Conclusion

Federated Learning is revolutionizing the way we approach machine learning, especially in scenarios where data privacy and security are of utmost importance. Whether you’re dealing with smartphones, IoT devices, or large-scale organizational collaboration, the different types of Federated Learning—Horizontal Federated Learning (HFL), Vertical Federated Learning (VFL), and Federated Transfer Learning (FTL)—provide flexible solutions for decentralized model training.

Understanding the types of Federated Learning helps in selecting the right approach for your specific use case, whether it’s improving predictive text without compromising privacy, collaborating across industries, or training models on edge devices. By choosing the correct type of Federated Learning, organizations can harness the power of machine learning while respecting data privacy and maintaining regulatory compliance.