隐私计算论文合集「联邦学习系列」第2期

前言：

隐语awesome-PETs（PETs即Privacy-Enhancing Technologies ，隐私增强技术）精选业内优秀论文，按技术类型进行整理分类，旨在为隐私计算领域的学习研究者提供一个高质量的学习交流社区。awesome-pets包含：安全多方计算(MPC)、零知识证明(ZKP)、联邦学习(FL)、差分隐私(DP)、可信执行环境(TEE)、隐私求交(PSI)等系列主题论文！

继上期[《多方安全计算》系列论文推荐活动小伙伴们参与热烈，社区收到了不少Paper留言。

本期继续带来联邦学习 (FL）系列论文推荐，更多主题Paper持续更新中ing～欢迎收藏项目。https://github.com/secretflow/secretflow/blob/main/docs/awesome-pets/awesome-pets.md

联邦学习系列论文

1、Survey

General

• Federated machine learning: Concept and applications

• Federated Learning in Mobile Edge Networks: A Comprehensive Survey

• Advances and Open Problems in Federated Learning

• Federated Learning: Challenges, Methods, and Future Directions

Security

• A survey on security and privacy of federated learning

• Threats to Federated Learning: A Survey

• Vulnerabilities in Federated Learning

由于篇幅原因，还有更多论文未能一一列举，请访问github收藏！https://github.com/secretflow/secretflow/blob/main/docs/awesome-pets/papers/applications/ppml/fl/fl.md

2、Datasets

• LEAF: A Benchmark for Federated Settings HomePage

• UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones HomePage

• The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems

• Evaluation Framework For Large-scale Federated Learning

• (*) PrivacyFL: A simulator for privacy-preserving and secure federated learning. MIT CSAIL.

• Revocable Federated Learning: A Benchmark of Federated Forest

3、Efficiency

Quantization

• Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

• Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning

• Communication Efficient Federated Learning with Adaptive Quantization

• QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

• DEED: A General Quantization Scheme for Communication Efficiency in Bits

4、Effectiveness

Model Aggregation

• FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data

• LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

• Federated Learning with Matched Averaging

• Federated Learning of a Mixture of Global and Local Models

• Faster On-Device Training Using New Federated Momentum Algorithm

• FedDANE: A Federated Newton-Type Method

• SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

5、Incentive

Contribution Evaluation

• Data Shapley: Equitable Valuation of Data for Machine Learning

• A principled approach to data valuation for federated learning

• Measure contribution of participants in federated learning

• GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

• Profit allocation for federated learning

• Fedcoin: A peer-to-peer payment system for federated learning

Profit Allocation

• Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile networks

• FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation

• Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction

6、Vertical FL

• SecureBoost: A Lossless Federated Learning Framework

• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

• Entity Resolution and Federated Learning get a Federated Resolution.

• Multi-Participant Multi-Class Vertical Federated Learning

• A Communication-Efficient Collaborative Learning Framework for Distributed Features

• Asymmetrical Vertical Federated Learning

7、Boosting

• Practical Federated Gradient Boosting Decision Trees

• Secureboost: A lossless federated learning framework

• Large-scale Secure XGB for Vertical Federated Learning

8、Application

Natural language Processing

• Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

• Federated Learning for Mobile Keyboard Prediction

• Federated Learning for Keyword Spotting

• generative sequence models (e.g., language models)

• Federated User Representation Learning

• Two-stage Federated Phenotyping and Patient Representation Learning

由于篇幅原因，还有更多论文未能一一列举，请访问github收藏！

https://github.com/secretflow/secretflow/blob/main/docs/awesome-pets/papers/applications/ppml/fl/fl.md