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https://github.com/JimLiu96/FederatedRS

Papers related to federated learning for recommender system
https://github.com/JimLiu96/FederatedRS

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Papers related to federated learning for recommender system

Host: GitHub
URL: https://github.com/JimLiu96/FederatedRS
Owner: JimLiu96
Created: 2021-04-07T03:10:08.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2021-04-25T00:51:00.000Z (over 3 years ago)
Last Synced: 2024-08-03T09:03:32.208Z (6 months ago)
Size: 43.9 KB
Stars: 32
Watchers: 5
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # Federated Learning Survey

| Year   | Title  | Venue | Code  |

|-------|--------|--------|-----------|

| 2021 | [Advances and Open Problems in Federated Learning](https://arxiv.org/pdf/1912.04977.pdf) | FTML  | Link |

| 2019 | [Federated Machine Learning: Concept and Applications](https://dl.acm.org/doi/pdf/10.1145/3298981) | TIST | Link |

# Federated Learning Papers

| Year   | Title  | Venue | Code  |

|-------|--------|--------|-----------|

| 2021 | [Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data](https://arxiv.org/pdf/2006.06983.pdf) | The Web  | Link |

| 2021 | [Hierarchical Federated Learning through LAN-WAN Orchestration](https://arxiv.org/pdf/2010.11612.pdf) | The Web  | Link |

| 2020 | [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://arxiv.org/pdf/2001.01523.pdf) | NeurIPS workshop | Link |

| 2020 | [Personalized Federated Learning: A Meta-Learning Approach](https://arxiv.org/pdf/2002.07948.pdf) | NeurIPS | Link | 

| 2017 | [Communication-Efficient Learning of Deep Networks from Decentralized Data](http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf) | AISTATS | [Link](https://github.com/shaoxiongji/federated-learning) |

# Federated  Recommender System

| Year   | Title  | Venue | Code  |

|-------|--------|--------|-----------|

| 2021 | [DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce](__) [\[Video\]](https://www.youtube.com/watch?v=2J61s9xXTPo) | The Web | Link |

| 2021 | [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://arxiv.org/abs/2102.04925) | arxiv | Link |

| 2020 | [Robust Federated Recommendation System](https://arxiv.org/pdf/2006.08259.pdf) | arxiv | Link |

| 2020 | [FEDERATED MULTI-VIEW MATRIX FACTORIZATION FOR PERSONALIZED RECOMMENDATIONS](https://arxiv.org/pdf/2004.04256.pdf) | arxiv | Link | 

| 2020 | [Secure Federated Matrix Factorization](https://arxiv.org/pdf/1906.05108.pdf) | Int. Sys. | [CODE](https://github.com/Di-Chai/FedMF) | 

| 2020 | [Privacy-Preserving News Recommendation Model Learning](https://arxiv.org/pdf/2003.09592.pdf) | EMNLP-Findings | [CODE](https://github.com/JulySinceAndrew/FedNewsRec-EMNLP-Findings-2020) | 

| 2019 | [FEDERATED COLLABORATIVE FILTERING FOR PRIVACY-PRESERVING PERSONALIZED RECOMMENDATION SYSTEM](https://arxiv.org/pdf/1901.09888.pdf) | arxiv | Link | 

| 2019 | [Federating Recommendations Using Differentially Private Prototypes](https://arxiv.org/pdf/2003.00602.pdf) | arxiv | Link | 

## Useful Resources

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

- https://github.com/tensorflow/federated

- https://github.com/AshwinRJ/Federated-Learning-PyTorch

- Flower https://flower.dev/

- PySyft https://github.com/OpenMined/PySyft

- Tensorflow Federated  https://www.tensorflow.org/federated

- CrypTen https://github.com/facebookresearch/CrypTen

- FATE https://fate.fedai.org/

- DVC https://dvc.org/

- LEAF https://leaf.cmu.edu/

- Federated iNaturalist/Landmarkds https://github.com/google-research/google-research/tree/master/federated_vision_datasets

- FedML: A Research Library and Benchmark for Federated Machine Learning https://github.com/FedML-AI/FedML

- XayNet: Open source framework for federated learning in Rust https://xaynet.webflow.io/ 

## Use-cases

MIT CSAIL/Harvard Medical/Tsinghua University’s Academy of Arts and Design

* https://arxiv.org/ftp/arxiv/papers/1903/1903.09296.pdf

* https://venturebeat.com/2019/03/25/federated-learning-technique-predicts-hospital-stay-and-patient-mortality/

Microsoft research/University of Chinese Academy of Sciences, Beijing, China

* https://arxiv.org/pdf/1907.09173.pdf

Boston University/Massachusetts General Hospital

* https://www.ncbi.nlm.nih.gov/pubmed/29500022

Google

* https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

* https://www.statnews.com/2019/09/10/google-mayo-clinic-partnership-patient-data/

Tencent WeBank

* https://www.digfingroup.com/webank-clustar/

Nvidia/King’s College London, American College of Radiology, MGH and BWH Center for Clinical Data Science, and UCLA Health... etc

* https://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/

* https://blogs.nvidia.com/blog/2019/12/01/clara-federated-learning/