--- title: "Federated Learning: Training Models Without Moving the Data" url: https://daily.dev/posts/federated-learning-training-models-without-moving-the-data-zrv9geuh3 source_url: https://www.codemotion.com/magazine/ai-ml/federated-learning-training-models-without-moving-the-data type: article source: "Codemotion" published: 2026-07-27T11:53:54.093Z updated: 2026-07-27T11:54:17.395Z tags: ["machine-learning", "deep-learning", "edge-computing"] reading_time: 6 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Federated Learning: Training Models Without Moving the Data **[Codemotion](https://daily.dev/sources/codemotion)** · 6 min read · 0 upvotes · 0 comments ## Summary Federated Learning (FL) inverts the traditional ML pipeline by sending the model to the data rather than centralizing data for training. Each participating client trains a local model copy and returns only weight updates or gradients — never raw data — to a central coordinator that aggregates them via algorithms like FedAvg. The architecture is already in production at Google, Apple, healthcare institutions, and financial systems. Key frameworks include TensorFlow Federated, Flower, PySyft, and NVIDIA FLARE. FL does not automatically guarantee privacy; known attack vectors like model inversion, membership inference, and gradient leakage require additional defenses such as differential privacy, secure aggregation, and homomorphic encryption. Real trade-offs include coordination overhead, non-IID data distribution across clients, and slower convergence, but FL offers a meaningfully smaller compliance surface and attack footprint for systems handling sensitive distributed data. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Federated Learning, Part 1: The Basics of Training Models Where the Data Lives](https://daily.dev/posts/federated-learning-part-1-the-basics-of-training-models-where-the-data-lives-qgopez7er) · Towards Data Science · 1 upvotes · 0 comments - [Federated Learning in ML](https://daily.dev/posts/federated-learning-in-ml-cbeipygrr) · Daily Dose of Data Science \| Avi Chawla \| Substack · 2 upvotes · 0 comments - [Federated Learning Without the Refactoring Overhead Using NVIDIA FLARE](https://daily.dev/posts/federated-learning-without-the-refactoring-overhead-using-nvidia-flare-ft4x8tgyh) · NVIDIA Developer · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#edge-computing](https://daily.dev/tags/edge-computing) [View this post on daily.dev](https://daily.dev/posts/federated-learning-training-models-without-moving-the-data-zrv9geuh3)