Federated learning

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. Related to terms such as "Edge ML" and "Frugal ML"

Resources

• https://en.wikipedia.org/wiki/Federated_learning
• Federated Learning: Collaborative Machine Learning without Centralized Training Data

Talks

• #TALK What is Federated Learning?
• #TALK Federated Learning: Machine Learning on Decentralized Data (Google I/O'19)

Code

• #CODE TF federated
  • https://www.tensorflow.org/federated?hl=es-419

References

• #PAPER Frugal Machine Learning (Evchenko 2021)
• #PAPER Federated learning challenges and opportunities: An outlook (Ding 2022)
  • Latest Paper From Amazon AI Research Analyzes And Explains The Challenges And Developments in The Field Of Federated Learning
• #PAPER Enabling Deep Learning on Edge Devices (Qu 2022)
• #PAPER Enabling All In-Edge Deep Learning: A Literature Review (Joshi 2022)
• #PAPER Intelligence at the Extreme Edge: A Survey on Reformable TinyML (Rajapakse 2023)