Federated-Learning
A collection of research papers, tutorials , blogs and Frameworks on FL
1.Contents
2.2 ByResearchArea
2.Papers
2.1Top-tier
CVPR2021
- Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning. Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, Vishal M. Patel
- Model-Contrastive Federated Learning. Qinbin Li, Bingsheng He, Dawn Song
- FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space. Quande Liu, Cheng Chen, Jing Qin, Qi Dou, Pheng-Ann Heng
- Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective. Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, Yiran Chen
ICML2020
- Federated Learning with Only Positive Labels;Google Research;2020;label deficiency in multi-class classification
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning;Google Research;2020;non-iid
- FedBoost: A Communication-Efficient Algorithm for Federated Learning;NYU & Google Research;2020;communication cost
- FetchSGD: Communication-Efficient Federated Learning with Sketching;UC Berkeley;2020;communication cost
- From Local SGD to Local Fixed-Point Methods for Federated Learning;KAUST;2020;communication cost
ICML2019
- Analyzing Federated Learning through an Adversarial Lens
- Bayesian Nonparametric Federated Learning of Neural Networks
- Agnostic Federated Learning
NeurIPS2020
- Personalized Federated Learning with Moreau Envelopes
- Lower Bounds and Optimal Algorithms for Personalized Federated Learning [KAUST]
- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach [MIT]
- Federated Principal Component Analysis [Cambridge]
- FedSplit: an algorithmic framework for fast federated optimization [Berkeley]
- Minibatch vs Local SGD for Heterogeneous Distributed Learning [Toyota]
- Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
- Throughput-Optimal Topology Design for Cross-Silo Federated Learning
- Distributed Distillation for On-Device Learning [Stanford]
- Ensemble Distillation for Robust Model Fusion in Federated Learning
- Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge [USC]
- Federated Accelerated Stochastic Gradient Descent [Github] [Stanford]
- Distributionally Robust Federated Averaging
- An Efficient Framework for Clustered Federated Learning [Berkeley]
- Robust Federated Learning: The Case of Affine Distribution Shifts [MIT]
- Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [CMU]
- Federated Bayesian Optimization via Thompson Sampling [NUS] [MIT]
- Distributed Newton Can Communicate Less and Resist Byzantine Workers [Berkeley]
- Byzantine Resilient Distributed Multi-Task Learning
- A Scalable Approach for Privacy-Preserving Collaborative Machine Learning [USC]
- Inverting Gradients - How easy is it to break privacy in federated learning?
- Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
- Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
- Differentially-Private Federated Linear Bandits [MIT]
NeurIPS2016-2019
- Federated Optimization: Distributed Optimization Beyond the Datacenter NIPS 2016 workshop
- Practical Secure Aggregation for Federated Learning on User-Held Data NIPS 2016 workshop
- Differentially Private Federated Learning: A Client Level Perspective NIPS 2017 Workshop
- Federated Multi-Task Learning NIPS 2017
- Deep Leakage from Gradients NIPS 2019
AAAI2021
- Federated Multi-Armed Bandits
- Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
- Provably Secure Federated Learning against Malicious Clients
- On the Convergence of Communication-Efficient Local SGD for Federated Learning
- Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
- Communication-Aware Collaborative Learning
- Peer Collaborative Learning for Online Knowledge Distillation
- A Communication Efficient Collaborative Learning Framework for Distributed Features
- Defending Against Backdoors in Federated Learning with Robust Learning Rate
- FLAME: Differentially Private Federated Learning in the Shuffle Model
- Toward Understanding the Influence of Individual Clients in Federated Learning
- Personalized Cross-Silo Federated Learning on Non-IID Data
- Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
- Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models
- Addressing Class Imbalance in Federated Learning
AAAI2020
- Federated Learning for Vision-‐and-‐Language Grounding Problems;2020
- Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework;2020
- Federated Patient Hashing;2020
- Robust Federated Learning via Collaborative Machine Teaching;2020
- Practical Federated Gradient Boosting Decision Trees;2020
IJCAI2021
- Collaborative Fairness in Federated Learning [IJCAI 2021 Workshop Best Paper]
- FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning [IJCAI 2021 Workshop Best Student Paper]
- Federated Learning with Diversified Preference for Humor Recognition [IJCAI 2021 Workshop Best Application Paper]
- Heterogeneous Data-Aware Federated Learning [IJCAI 2021 Workshop]
- Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention [IJCAI 2021 Workshop]
- FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training [IJCAI 2021 Workshop]
- FOCUS: Dealing with Label Quality Disparity in Federated Learning [IJCAI 2021 Workshop]
- Fed-Focal Loss for imbalanced data classification in Federated Learning [IJCAI 2021 Workshop]
- Threats to Federated Learning: A Survey [IJCAI 2021 Workshop]
- Asymmetrical Vertical Federated Learning [IJCAI 2021 Workshop]
- Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations [IJCAI 2021 Workshop]
- Achieving Differential Privacy in Vertically Partitioned Multiparty Learning [IJCAI 2021 Workshop]
- Privacy Threats Against Federated Matrix Factorization [IJCAI 2021 Workshop]
- TF-SProD: Time Fading based Sensitive Pattern Hiding in Progressive Data [IJCAI 2021 Workshop]
IJCAI2020
- Federated Meta-Learning for Fraudulent Credit Card Detection
- A Multi-player Game for Studying Federated Learning Incentive Schemes
ICLR2021
- Federated Learning Based on Dynamic Regularization
- Adaptive Federated Optimization
- Federated Learning via Posterior Avgeraging: A New Perspective and Practical Algorithms
- Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
- Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
- FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
- FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
- FedMix: Approximation of Mixup under Mean Augmented Federated Learning
- HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
- Personalized Federated Learning with First Order Model Optimization
KDD2020
- FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems
- Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
- FedCD: Improving Performance in non-IID Federated Learning [KDD20 Workshop]
- Resource-Constrained Federated Learning with Heterogeneous Labels and Models [KDD2020 Workshop]
2.2ByResearchArea
Survey
- Federated Machine Learning: Concept and Applications
- Federated Learning: Challenges, Methods, and Future Directions
- Advances and Open Problems in Federated Learning
- IBM Federated Learning: an Enterprise Framework White Paper V0.1
- Federated Learning White Paper V1.0
- Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- Survey of Personalization Techniques for Federated Learning. 2020-03-19
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey
- Threats to Federated Learning: A Survey
- An Introduction to Communication Efficient Edge Machine Learning
- Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
- A Review of Applications in Federated Learning
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
- Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
- 联邦学习算法综述,王健宗,孔令炜
VerticalFL
- "Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction,"Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura, Mar. 2020.
- "Federated Hierarchical Hybrid Networks for Clickbait Detection,"Feng Liao, Hankz Hankui Zhuo, Xiaoling Huang, and Yu Zhang, Jun. 2019.
- "Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,"Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne, Nov. 2017.
- "FDML: A Collaborative Machine Learning Framework for Distributed Features," Yaochen Hu, Di Niu, Jianming Yang, and Shengping Zhou,May 2019.
- "Learning Privately over Distributed Features: An ADMM Sharing Approach,"Yaochen Hu, Peng Liu, Linglong Kong, and Di Niu, Jul. 2019.
- "SplitFed: When Federated Learning Meets Split Learning,"Chandra Thapa, M. A. P. Chamikara, and Seyit Camtepe, Apr. 2020.
- "Privacy Enhanced Multimodal Neural Representations for Emotion Recognition,"Mimansa Jaiswal, and Emily Mower Provost, Oct. 2019.
- "Privacy-preserving Neural Representations of Text,"Maximin Coavoux, Shashi Narayan, and Shay B. Cohen, Aug. 2018.
- "PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training,"Meng Li, Liangzhen Lai, Naveen Suda, Vikas Chandra, and David Z. Pan, Sep. 2017.
- "One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction," Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura, Nov. 2019.
- "Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction," Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura, Mar. 2020.
- "Optimization for Large-Scale Machine Learning with Distributed Features and Observations," Alexandros Nathan, and Diego Klabjan, Apr. 2017.
- "SecureBoost: A Lossless Federated Learning Framework," Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Qiang Yang, Jan. 2019.
- "Privacy-Preserving Backpropagation Neural Network Learning," Tingting Chen, Sheng Zhong, Oct. 2009.
- "Split learning for health: Distributed deep learning without sharing raw patient data,"Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar, Dec. 2018.
Non-IID
- The Non-IID Data Quagmire of Decentralized Machine Learning;Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons;Microsoft Research;PMLR 2020
- Federated Learning with Non-IID Data;Yue Zhao,Vikas Chanra;San Jose, CA;arXiv 2018
- FedCD: Improving Performance in non-IID Federated Learning. 2020
- Survey of Personalization Techniques for Federated Learning
- Data selection for federated learning with relevant and irrelevant data at clients, Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, Kin K. Leung; arXiv 2020
- Three approaches for personalization with applications to federated learning, Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, arXiv 2020
- Life Long Learning: FedFMC: Sequential Efficient Federated Learning on Non-iid Data. 2020
- Personalized Federated Learning with Moreau Envelopes. 2020
- Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. 2020
- Towards Flexible Device Participation in Federated Learning for Non-IID Data. 2020
- NeurIPS 2020 submission: An Efficient Framework for Clustered Federated Learning
- FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
- Federated learning with hierarchical clustering of local updates to improve training on non-IID data
- Federated Learning with Only Positive Labels
- Adaptive Personalized Federated Learning
- Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
- Three Approaches for Personalization with Applications to Federated Learning
- Personalized Federated Learning: A Meta-Learning Approach
- Federated Learning with Personalization Layers
- Federated Evaluation of On-device Personalization
- Overcoming Forgetting in Federated Learning on Non-IID Data
- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Client Adaptation improves Federated Learning with Simulated Non-IID Clients
- Adaptive gradient-based meta-learning methods
- Salvaging federated learning by local adaptation
- Federated learning of a mixture of global and local models
- Federated multi-task learning
3.Tutorials
3.1book
- 杨强,刘洋,程勇,康焱,陈天健,于涵,《联邦学习》,电子工业出版社,2020年5月
- 隐私机密计算蓝皮书
- 隐私计算白皮书(2021)
- 2021隐私计算行业研究报告
3.2blogs
- 杨强,刘洋,陈天健,童咏昕,“联邦学习”,**计算机学会通讯
- **信息通信研究院《隐私保护计算与合规应用研究报告 (2021年)
- Federated Learning Comic
- Federated Learning: Collaborative Machine Learning without Centralized Training Data
- An Introduction to Federated Learning
- Online Comic from Google AI on Federated Learning
- GDPR, Data Shotrage and AI , AAAI-19
3.3slide
- 《隐私保护机器学习》slide
- 面向隐私安全保密的联邦学习和迁移学习
- 联邦学习的研究和应用
- 用非对称联邦保护客户隐私
- 云原生联邦学习的开源框架
- 联邦学习与安全多方计算
- FATE:联邦学习技术落地与应用
4.Frameworks
- PySyft
- Tensorflow Federated
- FATE; WeBank
- FedLearner ByteDance
- PaddleFL; Baidu
- LEAF: A Benchmark for Federated Settings
- FedML:A Research Library and Benchmark for Federated Machine Learning
- XayNe:Open source framework for federated learning in Rust
- PyTorch Federated Learning
- FedMA; IBM
- federated;Google Research
- Flower
5.Company&Application
公司 | 产品&地址 | 开源地址 |
---|---|---|
微众银行 | 联邦学习FATE | https://github.com/FederatedAI/FATE |
蚂蚁金服 | 摩斯多方安全计算平台 | - |
百度 | 联邦计算 | https://gitee.com/paddlepaddle/PaddleFL |
腾讯 | Angel PowerFL | - |
百度安全部门 | MesaTEE 安全计算平台 | https://github.com/apache/incubator-teaclave |
京东数科 | Fedlearn | - |
洞见智慧 | Insightone | - |
华控清交 | PrivPy 多方安全计算平台 | - |
字节跳动 | Fedlearner | https://github.com/bytedance/fedlearner |
富数科技 | FMPC | - |
矩阵元 | rosetta | https://github.com/LatticeX-Foundation/Rosetta |
数犊科技 | platone | - |
同盾科技 | 同盾智邦平台 | - |
致星科技 | 星云Clustar | - |