开源软件名称（OpenSource Name）：

poga/awesome-federated-learning

开源软件地址(OpenSource Url)：

https://github.com/poga/awesome-federated-learning

开源编程语言(OpenSource Language)：

开源软件介绍(OpenSource Introduction)：

Awesome Federated Learning

A list of resources releated to federated learning and privacy in machine learning.

Related Awesome Lists

tushar-semwal/awesome-federated-computing

Papers

Introduction & Survey

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies https://ieeexplore.ieee.org/document/9780218
The Internet of Federated Things (IoFT) https://ieeexplore.ieee.org/document/9611259
Advances and Open Problems in Federated Learning https://arxiv.org/pdf/1912.04977.pdf
Federated Machine Learning: Concept and Applications https://arxiv.org/pdf/1902.04885
Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection https://arxiv.org/abs/1907.09693
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis https://arxiv.org/abs/1802.09941
EdgeAI: A Visionfor Deep Learning in IoT Era https://arxiv.org/abs/1910.10356
Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data https://arxiv.org/abs/1910.08663
No Peek: A Survey of private distributed deep learning https://arxiv.org/pdf/1812.03288
Federated Learning in Mobile Edge Networks: A Comprehensive Survey https://arxiv.org/abs/1909.11875

Privacy and Security

Federated Learning with Formal Differential Privacy Guarantees https://ai.googleblog.com/2022/02/federated-learning-with-formal.html
Applying Differential Privacy to Large Scale Image Classification https://ai.googleblog.com/2022/02/applying-differential-privacy-to-large.html
Towards Causal Federated Learning For Enhanced Robustness And Privacy https://arxiv.org/pdf/2104.06557.pdf ICLR DPML 2021
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning https://arxiv.org/abs/2102.02514
OpenFL: An open-source framework for Federated Learning https://arxiv.org/abs/2105.06413
A Bayesian Federated Learning Framework with Multivariate Gaussian Product https://arxiv.org/abs/2102.01936
Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf
Practical Secure Aggregation for Federated Learning on User-Held Data https://arxiv.org/abs/1611.04482
Practical Secure Aggregation for Privacy-Preserving Machine Learning https://storage.googleapis.com/pub-tools-public-publication-data/pdf/ae87385258d90b9e48377ed49d83d467b45d5776.pdf
A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/abs/1812.03224
Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/pdf/1811.12470
How To Backdoor Federated Learning https://arxiv.org/abs/1807.00459
Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attack https://arxiv.org/abs/1812.00910
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535
Exploiting Unintended Feature Leakage in Collaborative Learning https://arxiv.org/abs/1805.04049
Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/abs/1811.12470
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning https://arxiv.org/abs/1702.07464
Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing https://arxiv.org/abs/1907.10218
Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274
Differentially Private Federated Learning: A Client Level Perspective https://arxiv.org/abs/1712.07557
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants https://arxiv.org/abs/1812.10113
Scalable Private Learning with PATE https://arxiv.org/abs/1802.08908
Reducing leakage in distributed deep learning for sensitive health data https://www.media.mit.edu/publications/reducing-leakage-in-distributed-deep-learning-for-sensitive-health-data-accepted-to-iclr-2019-workshop-on-ai-for-social-good-2019/
Deep Leakage from Gradients http://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning https://arxiv.org/abs/1805.05838

System and Application

Record and Reward Federated Learning Contributions with Blockchain https://mblocklab.com/RecordandReward.pdf
Flower: A Friendly Federated Learning Framework https://arxiv.org/pdf/2007.14390.pdf
Learning Private Neural Language Modeling with Attentive Aggregation https://arxiv.org/pdf/1812.07108
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning https://arxiv.org/abs/2003.09603
Decentralized Knowledge Acquisition for Mobile Internet Applications https://link.springer.com/article/10.1007/s11280-019-00775-w
A generic framework for privacy preserving deep learning https://arxiv.org/pdf/1811.04017.pdf
Federated Learning of N-gram Language Models https://arxiv.org/pdf/1910.03432.pdf
Towards Federated Learning at Scale: System Design https://arxiv.org/pdf/1902.01046.pdf
Federated Learning for Keyword Spotting https://arxiv.org/abs/1810.05512
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data https://arxiv.org/abs/1810.08553
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System https://arxiv.org/pdf/1901.09888
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence https://arxiv.org/abs/1910.02109
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platform http://www.cs.ucf.edu/~mohaisen/doc/dsn19b.pdf
Institutionally Distributed Deep Learning Networks https://arxiv.org/abs/1709.05929
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation https://arxiv.org/abs/1810.04304
Split learning for health: Distributed deep learning without sharing raw patient data https://www.media.mit.edu/publications/split-learning-for-health-distributed-deep-learning-without-sharing-raw-patient-data/
Continuous Delivery for Machine Learning https://martinfowler.com/articles/cd4ml.html#EvolvingIntelligentSystemsWithoutBias
Ease.ml/ci & Ease.ml/meter Towards Data Management for Statistical Generialization http://ease.ml/
VisionAir: Using Federated Learning to estimate Air Quality using the Tensorflow API for Java https://blog.tensorflow.org/2020/02/visionair-using-federated-learning-to-estimate-airquality-tensorflow-api-java.html
Federated Optimization in Heterogeneous Networks https://arxiv.org/abs/1812.06127

Un-org

FedProf: Optimizing Federated Learning with Dynamic Data Profiling https://arxiv.org/abs/2102.01733
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization https://arxiv.org/abs/2102.07623
A Scalable Approach for Partially Local Federated Learning https://ai.googleblog.com/2021/12/a-scalable-approach-for-partially-local.html?m=1
Federated Visual Classification with Real-World Data Distribution https://arxiv.org/abs/2003.08082
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification https://arxiv.org/abs/1909.06335
LEAF: A Benchmark for Federated Settings https://arxiv.org/abs/1812.01097
On the Convergence of FedAvg on Non-IID Data https://arxiv.org/abs/1907.02189
Privacy-preserving Federated Brain Tumour Segmentation. https://arxiv.org/pdf/1910.00962.pdf
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries https://www.media.mit.edu/publications/ExpertMatcher/
Detailed comparison of communication efficiency of split learning and federated learning https://www.media.mit.edu/publications/detailed-comparison-of-communication-efficiency-of-split-learning-and-federated-learning-1/
Split Learning: Distributed and collaborative learning https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf
Asynchronous Federated Optimization https://arxiv.org/pdf/1903.03934
Robust and Communication-Efficient Federated Learning from Non-IID Data https://arxiv.org/pdf/1903.02891
One-Shot Federated Learning https://arxiv.org/pdf/1902.11175
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions https://arxiv.org/pdf/1902.08999
Agnostic Federated Learning https://arxiv.org/pdf/1902.00146%C2%A0
Peer-to-peer Federated Learning on Graphs https://arxiv.org/pdf/1901.11173
SecureBoost: A Lossless Federated Learning Framework https://arxiv.org/pdf/1901.08755
Federated Reinforcement Learning https://arxiv.org/pdf/1901.08277
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems https://arxiv.org/pdf/1901.06455
Federated Learning via Over-the-Air Computation https://arxiv.org/pdf/1812.11750
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) https://arxiv.org/pdf/1812.11494
Multi-objective Evolutionary Federated Learning https://arxiv.org/pdf/1812.07478
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach https://arxiv.org/pdf/1812.03633
A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/pdf/1812.03224
Applied Federated Learning: Improving Google Keyboard Query Suggestions https://arxiv.org/pdf/1812.02903
Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274
Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984
Split learning for health: Distributed deep learning without sharing raw patient data https://arxiv.org/pdf/1812.00564
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535
LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data https://arxiv.org/pdf/1811.12629
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data https://arxiv.org/pdf/1811.11479
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning https://arxiv.org/pdf/1811.09904
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting https://arxiv.org/pdf/1811.09712
Federated Learning Approach for Mobile Packet Classification https://arxiv.org/abs/1907.13113
Collaborative Learning on the Edges: A Case Study on Connected Vehicles https://www.usenix.org/conference/hotedge19/presentation/lu
Federated Learning for Time Series Forecasting Using Hybrid Model http://www.diva-portal.se/smash/get/diva2:1334629/FULLTEXT01.pdf
Federated Learning: Challenges, Methods, and Future Directions https://arxiv.org/pdf/1908.07873.pdf
Federated Learning with Matched Averaging https://openreview.net/forum?id=BkluqlSFDS