Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Abstract
Federated learning has been a hot research area 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. Just like deep learning systems such as Caffe,
PyTorch, and Tensorflow that boost the development of deep learning algorithms,
federated learning systems are equivalently important, and face challenges from
various issues such as unpractical system assumptions, scalability and
efficiency. Inspired by federated systems in other fields such as databases and
cloud computing, we investigate the existing characteristics of federated
learning systems. We find that two important features for federated systems in
other fields, i.e., heterogeneity and autonomy, are rarely considered in the
existing federated learning systems. Moreover, we provide a thorough
categorization for federated learning systems according to six different
aspects, including data partition, 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. Lastly, we take a systematic comparison among the existing
federated learning systems and present…
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