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Papers related to federated learning for recommender system

Federated Learning Survey

Year Title Venue Code
2021 Advances and Open Problems in Federated Learning FTML Link
2019 Federated Machine Learning: Concept and Applications TIST Link

Federated Learning Papers

Year Title Venue Code
2021 Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data The Web Link
2021 Hierarchical Federated Learning through LAN-WAN Orchestration The Web Link
2020 Think Locally, Act Globally: Federated Learning with Local and Global Representations NeurIPS workshop Link
2020 Personalized Federated Learning: A Meta-Learning Approach NeurIPS Link
2017 Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS Link

Federated Recommender System

Year Title Venue Code
2021 DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce [Video] The Web Link
2021 FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation arxiv Link
2020 Robust Federated Recommendation System arxiv Link
2020 FEDERATED MULTI-VIEW MATRIX FACTORIZATION FOR PERSONALIZED RECOMMENDATIONS arxiv Link
2020 Secure Federated Matrix Factorization Int. Sys. CODE
2020 Privacy-Preserving News Recommendation Model Learning EMNLP-Findings CODE
2019 FEDERATED COLLABORATIVE FILTERING FOR PRIVACY-PRESERVING PERSONALIZED RECOMMENDATION SYSTEM arxiv Link
2019 Federating Recommendations Using Differentially Private Prototypes 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/