# federated learning

> method for artificial intelligence

**Wikidata**: [Q50818671](https://www.wikidata.org/wiki/Q50818671)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Federated_learning)  
**Source**: https://4ort.xyz/entity/federated-learning

## Summary
Federated learning is a method for artificial intelligence that enables machine learning models to be trained across decentralized devices or servers while keeping data localized. It is a subclass of both artificial intelligence and machine learning, focusing on privacy, decentralization, scalability, and personalization. This approach is particularly valuable in scenarios where data cannot be centralized due to privacy concerns or regulatory restrictions.

## Key Facts
- Federated learning is a subclass of artificial intelligence and machine learning.
- It is also classified as a distributed system.
- The primary goals of federated learning include privacy, decentralization, scalability, and personalization.
- It is associated with the GitHub topic "federated-learning," first referenced on July 25, 2021.
- The MeSH tree codes for federated learning are G17.035.250.500.469 and L01.224.050.375.530.469, both qualified by "machine learning."
- The MeSH descriptor ID for federated learning is D000098407.
- Federated learning has Wikipedia articles in Arabic, Catalan, German, English, Spanish, Persian, French, Italian, Swedish, Ukrainian, and Chinese.
- The Google Knowledge Graph IDs for federated learning are /g/11ggs27l_s and /g/11hyd49kls.
- The Encyclopedia of China (Third Edition) ID for federated learning is 413472.
- Federated learning is known by aliases such as aprendizaje federado de cohorts, تعلم متحد, التعلم الموحد, التعلم التعاوني, and التعلم المتعاون.
- The sitelink count for federated learning is 12.

## FAQs
**What is the primary purpose of federated learning?**
Federated learning aims to enable machine learning models to be trained across decentralized devices or servers while keeping data localized, addressing privacy and regulatory concerns.

**How does federated learning differ from traditional machine learning?**
Unlike traditional machine learning, which requires centralized data, federated learning allows models to be trained on decentralized data sources, enhancing privacy and scalability.

**What are the key goals of federated learning?**
The key goals of federated learning include privacy, decentralization, scalability, and personalization, making it suitable for scenarios where data cannot be centralized.

**Which fields is federated learning a subclass of?**
Federated learning is a subclass of artificial intelligence, machine learning, and distributed systems.

**What are some of the aliases for federated learning?**
Federated learning is also known as aprendizaje federado de cohorts, تعلم متحد, التعلم الموحد, التعلم التعاوني, and التعلم المتعاون.

## Why It Matters
Federated learning addresses critical challenges in data privacy and decentralized computing by allowing models to be trained on distributed data without compromising user privacy. This method is particularly relevant in healthcare, finance, and IoT applications where data cannot be centralized due to regulatory or security concerns. By enabling personalized and scalable machine learning, federated learning plays a pivotal role in advancing AI technologies while respecting data sovereignty.

## Notable For
- Federated learning is distinguished by its focus on privacy and decentralization, making it unique in the field of machine learning.
- It is classified as a distributed system, highlighting its ability to operate across multiple devices or servers.
- The GitHub topic "federated-learning" reflects its growing adoption and community engagement in the tech ecosystem.
- The MeSH tree codes and descriptor ID indicate its recognition in the medical and scientific literature.
- The availability of Wikipedia articles in multiple languages underscores its global relevance and understanding.

## Body
### Classification and Relationships
Federated learning is a specialized method within the broader fields of artificial intelligence and machine learning. It is explicitly classified as a subclass of both artificial intelligence and machine learning, as well as a distributed system. This classification emphasizes its ability to operate across decentralized environments while maintaining data privacy.

### Goals and Applications
The primary goals of federated learning include privacy, decentralization, scalability, and personalization. These objectives make it particularly valuable in applications such as healthcare, finance, and IoT, where data cannot be centralized due to regulatory or security concerns. By enabling models to be trained on distributed data, federated learning enhances both privacy and scalability.

### Digital Presence and Recognition
Federated learning is recognized through various digital identifiers, including the Google Knowledge Graph IDs /g/11ggs27l_s and /g/11hyd49kls. It is also referenced in the Encyclopedia of China (Third Edition) under ID 413472. The MeSH tree codes G17.035.250.500.469 and L01.224.050.375.530.469, both qualified by "machine learning," further highlight its scientific recognition.

### Language and Cultural Representation
Federated learning has Wikipedia articles in multiple languages, including Arabic, Catalan, German, English, Spanish, Persian, French, Italian, Swedish, Ukrainian, and Chinese. This linguistic diversity reflects its global relevance and understanding across different cultural and linguistic contexts.

### Aliases and Terminology
Federated learning is known by various aliases, such as aprendizaje federado de cohorts, تعلم متحد, التعلم الموحد, التعلم التعاوني, and التعلم المتعاون. These aliases highlight its recognition and adoption in different regions and languages.

### Community and Adoption
The presence of the GitHub topic "federated-learning" indicates its growing adoption and community engagement in the tech ecosystem. This topic was first referenced on July 25, 2021, marking an important milestone in its development and adoption.

### Significance in Machine Learning
Federated learning plays a significant role in the field of machine learning by addressing critical challenges in data privacy and decentralized computing. Its ability to enable personalized and scalable machine learning makes it a key innovation in the evolution of AI technologies.

## References

1. [federated-learning · GitHub Topics · GitHub](https://github.com/topics/federated-learning)
2. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)