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Everything about Federated learning

Decentralized machine learning

Federated learning is a machine learning technique in a setting where multiple entities collaboratively train a model while keeping their data decentralized, rather than centrally stored. A defining characteristic of federated learning is data heterogeneity. Because client data is decentralized, data samples held by each client may not be independently and identically distributed. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Federated learningA 5-minute orientation
  2. Skim a recent paper: Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated LearningInfluential work since 2022
  3. Pick up a classic book: [Nachalo]The most-reprinted book on the subject
  4. Read the landmark paper: Federated Machine LearningCited 5,913 times
  5. Go sideways: Local differential privacyA closely related topic
  6. Go sideways: Graph neural networkA closely related topic

📖 Definitions

Noun A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics