---
title: "Scalability"
description: "Scalability ensures systems grow with demand. In Federated Learning, it involves efficiently managing dynamic clients and diverse devices. Flower supports large-scale FL on various devices/ resources."
date: "2024-05-23"
author:
  name: "Daniel Nata Nugraha"
  position: "Software Engineer"
  image: "daniel_nata_nugraha.png"
  website: "https://www.linkedin.com/in/daniel-nugraha/"
  github: "github.com/danielnugraha"
related:
  - text: "Flower Paper"
    link: "https://arxiv.org/pdf/2007.14390"
  - text: "Federated Learning"
    link: "/glossary/federated-learning"
  - text: "Tutorial: What is Federated Learning?"
    link: "/docs/framework/tutorial-series-what-is-federated-learning.html"
---

Scalability is the ability of a system, network, or process to accommodate an increasing amount of work. This involves adding resources (like servers) or optimizing existing ones to maintain or enhance performance. There are two main types of scalability: horizontal scalability (adding more nodes, such as servers) and vertical scalability (adding more power to existing nodes, like increasing CPU or RAM). Ideally, a scalable system can do both, seamlessly adapting to increased demands without significant downtime. Scalability is essential for businesses to grow while ensuring services remain reliable and responsive.
Scalability in Federated Learning involves managing dynamic client participation, as clients may join or leave unpredictably. This requires algorithms that adapt to varying availability and efficiently aggregate updates from numerous models. Additionally, scalable federated learning systems must handle heterogeneous client devices with different processing powers, network conditions, and data distributions, ensuring balanced contributions to the global model.
Scalability in Flower means efficiently conducting large-scale federated learning (FL) training and evaluation. Flower enables researchers to launch FL experiments with many clients using reasonable computing resources, such as a single machine or a multi-GPU rack. Flower supports scaling workloads to millions of clients, including diverse devices like Raspberry Pis, Android and iOS mobile devices, laptops, etc. It offers complete control over connection management and includes a virtual client engine for large-scale simulations.
