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1. A Survey on Federated Learning
Systems: Vision, Hype and Reality for
Data Privacy and Protection
Abstract
As data privacy increasingly becomes a critical societal concern, 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 researcher
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
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
A Survey on Federated Learning
Systems: Vision, Hype and Reality for
Data Privacy and Protection
As data privacy increasingly becomes a critical societal concern, 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
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
comprehensive review on federated learning systems. To
Systems: Vision, Hype and Reality for
As data privacy increasingly becomes a critical societal concern, federated
learning has been a hot research topic in enabling the collaborative training of
machine learning models among different organizations under the privacy
s try to support more machine learning models with
preserving approaches, there is a requirement in developing
systems and infrastructures to ease the development of various federated
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
comprehensive review on federated learning systems. To

2. understand the key design system components 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.