联邦学习论文汇总

总结一下从网络上收集来的联邦学习的论文，整体可能质量参差不齐

• Google的第一篇论文：

  Communication-Efficient Learning of Deep Networks from Decentralized Data

• 综述类型：

  Advances and Open Problems in Federated Learning

  Federated machine learning: Concept and applications

• 联邦学习算法与通信优化：

  Privacy-Preserving Deep Learning

  Agnostic Federated Learning

  Bayesian Nonparametric Federated Learning of Neural Networks

  Federated learning with matched averaging (该文内容基于上面一篇文章的工作)

  Federated learning: Strategies for improving communication efficiency

  Federated multi-task learning

  Federated optimization in heterogeneous networks

  Fair resource allocation in federated learning （与上面一篇同作者）

  Communication-Efficient Federated Learning with Sketching

  FedBoost: Communication-Efficient Algorithms for Federated Learning

  Federated Learning with Only Positive Labels

  Scaffold: Stochastic controlled averaging for federated learning

  Federated Meta-Learning for Fraudulent Credit Card Detection

  Federated Learning with Communication Delay in Edge Networks

  FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework

• 联邦元学习：

  Federated learning with personalization layers

  Improving federated learning personalization via model agnostic meta learning

• 联邦学习安全问题：

  Deep models under the GAN: information leakage from collaborative deep learning

  How to backdoor federated learning

  Quantification of the Leakage in Federated Learning

  Analyzing federated learning through an adversarial lens

  Deep leakage from gradients

• 联邦学习公平与贡献评估

  A Multi-player Game for Studying Federated Learning Incentive Schemes

  A Real-time Contribution Measurement Method for Participants in Federated Learning

  Collaborative Fairness in Federated Learning

  Hierarchically Fair Federated Learning

  Incentive design for efficient federated learning in mobile networks: A contract theory approach

  Measure contribution of participants in federated learning

  Profit Allocation for Federated Learning

  Interpret federated learning with shapley values

• 联邦学习与计算机视觉

  Federated Learning for Vision-and-Language Grounding Problems.

  Performance Optimization for Federated Person Re-identification via Benchmark Analysis

• 联邦学习与推荐系统

  FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

整体来说，开山之作Communication-Efficient Learning of Deep Networks from Decentralized Data和综合概述Advances and Open Problems in Federated Learning是需要优先阅读的。进度先记录到这里。