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
前往github提交PR，推荐“联邦学习”主题论文，私信隐语小助手SecretFlow01，参与抽奖活动！（中奖率超高呦～奖品见下文）且推荐论文被合并的贡献者，还将在隐语官方repo中进行@，以肯定及感谢您在awesome-pets项目中的贡献成果。https://github.com/secretflow/secretflow/pulls
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！
