上传者: ruiyiin
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上传时间: 2021-04-14 14:56:29
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文件大小: 1.39MB
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文件类型: PDF
Advances and Open Problems in Federated Learning。Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or
whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service
provider), while keeping the training data decentralized. FL embodies the principles of focused data
collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting
from traditional, centralized machine learning and data science approaches. Motivated by the explosive
growth in FL research, this paper discusses recent advances and presents an extensive collection of open
problems and challenges.