Federated Learning


  1. Secure, privacy-preserving and federated machine learning in medical imaging – Link
  2. Federated Learning – Privacy-Preserving Collaborative Machine, Learning without Centralized Training Data – Link
  3. Federated Learning in Healthcare (WiSe2020) – Link


  1. Flower: A Friendly Federated Learning Framework – Link
  2. [https://federated.withgoogle.com/] – Federated Learning – Learning better products with on-device data and privacy by default. An online comic from Google AI


  1. Towards Federated Learning at Scale: System Design by Keith Bonawitz et al. – Link
  2. Robust De-anonymization of Large Sparse Datasets by Arvind Narayanan et al. – Link
  3. Communication-Efficient Learning of Deep Networks from Decentralized Data by H. Brendan McMahan et al. – Link
  4. Differential Privacy and Machine Learning: a Survey and Review by Zhanglong Ji et al. – Link
  5. Federated Learning: Challenges, Methods, and Future Directions by Tian Li et al. – Link
  6. Federated Optimization in Heterogeneous Networks by Tian Li et al. – Link
  7. Privacy-Preserving Deep Learning by Reza Shokri et al. – Link

The main difference between federated learning and distributed learning lies in the assumptions made on the properties of the local datasets, as distributed learning originally aims at parallelizing computing power where federated learning originally aims at training on heterogeneous datasets.” – Wikipedia