---
title: "Introduction To Federated Learning"
url: https://daily.dev/posts/introduction-to-federated-learning-9ts89zmgv
source_url: https://towardsai.net/p/machine-learning/introduction-to-federated-learning-2
type: article
source: "Towards AI"
published: 2025-02-24T23:25:16.498Z
updated: 2025-02-24T23:26:04.496Z
tags: ["ai", "machine-learning", "privacy", "healthcare", "distributed-systems"]
reading_time: 12
upvotes: 1
comments: 0
language: en
---

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# Introduction To Federated Learning

**[Towards AI](https://daily.dev/sources/tai)** · 12 min read · 1 upvotes · 0 comments

## Summary

Federated learning is a decentralized approach to AI training that addresses the issue of limited access to high-quality data while preserving data privacy. It allows multiple entities to collaboratively train AI models by keeping data localized and only sharing model updates. This method overcomes logistical and privacy-related challenges, making it valuable for industries like healthcare, finance, and automotive. Federated learning relies on secure aggregation techniques and handles data heterogeneity, representing a significant advancement in privacy-conscious AI development.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsai.net/p/machine-learning/introduction-to-federated-learning-2>

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---

Tags: [#ai](https://daily.dev/tags/ai), [#machine-learning](https://daily.dev/tags/machine-learning), [#privacy](https://daily.dev/tags/privacy), [#healthcare](https://daily.dev/tags/healthcare), [#distributed-systems](https://daily.dev/tags/distributed-systems)

[View this post on daily.dev](https://daily.dev/posts/introduction-to-federated-learning-9ts89zmgv)