Federated Learning is a distributed machine learning approach which enables
model training on a large corpus of decentralized data. We have built a
scalable production system for Federated Learning in the domain of mobile
devices, based on TensorFlow. In this paper, we describe the resulting
high-level design, sketch some of the challenges and their solutions, and touch
upon the open problems and future directions.

本文描述了在移动设备领域基于 TensorFlow 构建的分布式机器学习方法 —— 联邦学习的高级设计，以及所面临的挑战、解决方案和未来研究方向。

系统设计：朝着大规模联邦学习迈进

Towards Federated Learning at Scale: System Design

In large-scale statistical learning, data collection and model fitting are
moving increasingly toward peripheral devices---phones, watches, fitness
trackers---away from centralized data collection. Concomitant with this rise in
decentralized data are increasing challenges of maintaining privacy while
allowing enough information to fit accurate, useful statistical models. This
motivates local notions of privacy---most significantly, local differential
privacy, which provides strong protections against sensitive data
disclosures---where data is obfuscated before a statistician or learner can
even observe it, providing strong protections to individuals' data. Yet local
privacy as traditionally employed may prove too stringent for practical use,
especially in modern high-dimensional statistical and machine learning
problems. Consequently, we revisit the types of disclosures and adversaries
against which we provide protections, considering adversaries with limited
prior information and ensuring that with high probability, ensuring they cannot
reconstruct an individual's data within useful tolerances. By reconceptualizing
these protections, we allow more useful data release---large privacy parameters
in local differential privacy---and we design new (minimax) optimal locally
differentially private mechanisms for statistical learning problems for
\emph{all} privacy levels. We thus present practicable approaches to
large-scale locally private model training that were previously impossible,
showing theoretically and empirically that we can fit large-scale image
classification and language models with little degradation in utility.

本文探讨了在分散式数据环境下，采用局部差分隐私保护敏感数据的可行性，设计了最优局部差分隐私机制，实现了在大规模数据训练下保护隐私，同时保证模型准确性。