# A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

## TL;DR

This survey comprehensively reviews federated learning systems, analyzing their components, categorization, and challenges, to guide future research in privacy-preserving collaborative machine learning.

## Contribution

It introduces a systematic categorization of federated learning systems based on six aspects and provides a detailed analysis of their design factors and future opportunities.

## Key findings

- Categorization of federated learning systems into six aspects.
- Analysis of system components and design factors.
- Identification of future research directions.

## Abstract

Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.