EXPANDING THE REACH OF FEDERATED LEARNING.pdf

联邦学习，随机梯度下降

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.

差异化隐私的联合学习 引文 如果您发现“ DP联合学习”在您的研究中很有用，请考虑引用： @ARTICLE{Wei2020Fed, author={Kang Wei and Jun Li and Ming Ding and Chuan Ma and Howard H. Yang and Farhad Farokhi and Shi Jin and Tony Q. S. Quek and H. Vincent Poor}, journal={{IEEE} Transactions on Information Forensics and Security}, title={Federated Learning with Differential Privacy: {Algorithms} and Performance Analysis}, year={2020}, vo

联邦学习国际标准-OpenMPC：IEEE Guide for Architectural Framework and Application of Federated Machine Learning

最近来自斯坦福、CMU、Google等25家机构58位学者共同发表了关于联邦学习最新进展与开放问题的综述论文《Advances and Open Problems in Federated Learning》，共105页pdf调研了438篇文献，讲解了最新联邦学习进展，并提出大量开放型问题。