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Federated Learning -- Methods, Applications and beyond

arXiv 2212.11729 ↗First posted 22 Dec 2022cs.LG stat.MLcited by 0

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Abstract (arXiv)

In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress.While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated. In particular data privacy plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training data is often distributed in terms of features or samples and unavailable for classicalbatch learning approaches. In 2016 Google came up with a framework, called Federated Learning to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal Federated Learning, as well as Fderated Transfer Learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 22 Dec 2022.

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26 of 51 references.

References · 26

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  9. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  10. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  11. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  12. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
14 more
  1. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  2. 2019Federated Adversarial Domain Adaptationcited by 140
  3. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 124
  4. 2018Blockchained On-Device Federated Learningcited by 106
  5. 2018Federated Learning for Keyword Spottingcited by 95
  6. 2019Accelerating Federated Learning via Momentum Gradient Descentcited by 76
  7. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 75
  8. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 72
  9. 2018Multi-objective Evolutionary Federated Learningcited by 42
  10. 2019Abnormal Client Behavior Detection in Federated Learningcited by 40
  11. 2020Federated Transfer Learning for EEG Signal Classificationcited by 37
  12. 2019Federated Learning for Ranking Browser History Suggestionscited by 24
  13. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  14. 2021Continual Learning at the Edge: Real-Time Training on Smartphone Devicescited by 20

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