mlsanityData to 14 Oct 2026

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

arXiv 2108.10761 ↗First posted 24 Aug 2021cs.DC cs.AI cs.CRcited by 8

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Abstract (arXiv)

Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

35 of 63 references.

References · 35

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2018How To Backdoor Federated Learningcited by 765
  8. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  10. 2020The Future of Digital Health with Federated Learningcited by 633
  11. 2019Federated Learning with Personalization Layerscited by 541
  12. 2020An Efficient Framework for Clustered Federated Learningcited by 511
23 more
  1. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  2. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  3. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  4. 2019Federated Machine Learning: Concept and Applicationscited by 412
  5. 2020iDLG: Improved Deep Leakage from Gradientscited by 382
  6. 2020Adaptive Personalized Federated Learningcited by 358
  7. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  8. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  9. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  10. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  11. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  12. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  13. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  14. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 148
  15. 2018Stronger Data Poisoning Attacks Break Data Sanitization Defensescited by 135
  16. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  17. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  18. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 78
  19. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 64
  20. 2019Abnormal Client Behavior Detection in Federated Learningcited by 40
  21. 2017Federated Tensor Factorization for Computational Phenotypingcited by 21
  22. 2018Secure Federated Transfer Learningcited by 13
  23. 2019Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortalitycited by 7

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 0; 2025: 1.

8 papers.

  1. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  2. 2023Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Datacited by 40
  3. 2022On the Convergence of Clustered Federated Learningcited by 31
  4. 2022Personalized Federated Learning With Graphcited by 29
  5. 2023Dual Personalization on Federated Recommendationcited by 19
  6. 2021Sequential Diagnosis Prediction with Transformer and Ontological Representationcited by 12
  7. 2023Breaching FedMD: Image Recovery via Paired-Logits Inversion Attackcited by 7
  8. 2025FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Datacited by 1