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)

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

   2.2 ByResearchArea

   • Survey
   • VerticalFL
   • HorizontalFL

3. Tutorials

   3.1 book

   3.2 blogs

   3.3 ppt

4. Frameworks

5. Company & Application

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2.Papers

2.1Top-tier

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](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 Averaging: 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

VerticalFL

HorizontalFL

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

3.2blogs

• 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)

3.3ppt

• ADL115《隐私保护机器学习》ppt

4.Frameworks

• PySyft
• Tensorflow Federated
• FATE; WeBank
• FedLearner ByteDance
• PaddleFL; Baidu
• LEAF
• FedML:A Research Library and Benchmark for Federated Machine Learning
• XayNe:Open source framework for federated learning in Rust
• PyTorch Federated Learning
• [FedMA](https://github.com/IBM/federated-learning-lib); IBM
• federated;Google Research
• Flower

5.Company&Application