Federated-Learning

A collection of research papers, tutorials , blogs and Frameworks on FL

1.Contents

1. Contents

2. ResearchPapers

   2.1 Top-tier(ICML, NeurIPS, ICLR, CVPR, IJCAI, AAAI)

   • CVPR2021
   • ICML2020
   • ICML2019
   • NeurIPS2020
   • NeurIPS2016-2019
   • AAAI2021
   • AAAI2020
   • IJCAI2021
   • IJCAI2020
   • ICLR2021
   • KDD2020

   2.2 ByResearchArea

   • Survey
   • VerticalFL
   • Non-IID

3. Tutorials

   3.1 book

   3.2 IndustryReport

   3.3 blogs

   3.4 slide

4. Frameworks

5. Company & Application

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
• 彭南博，王虎等. 《联邦学习技术及实战》. 电子工业出版社. 2021年3月
• 杨强，黄安埠，刘洋，陈天健等. 《联邦学习实战》. 电子工业出版社. 2021年5月
• 王建宗，李泽远，何安珣. 《深入浅出联邦学习：原理与实践》. 机械工业出版社. 2021年5月

3.2IndustryReport

• 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
• 全球数字经济白皮书
• 2021全球联邦学习研究与应用趋势报告
• 腾讯隐私计算白皮书

3.3blogs

• 杨强，刘洋，陈天健，童咏昕，“联邦学习”，中国计算机学会通讯
• 中国信息通信研究院《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

3.4slide

• 《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

5.Company&Application