Federated learning

Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) 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.

Federated learning - Wikipedia Jump to content From Wikipedia, the free encyclopedia Decentralized machine learning Diagram of a Federated Learning protocol with smartphones training a global AI model Federated learning (also known as collaborative learning ) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized , [ 1 ] rather than centrally stored. A defining characteristic of federated learning is data heterogeneity . Because client data is decentralized, data samples held by each clien

related reading

• How Federated Learning Improves AI Without Centralizing Sensitive Dataxenonstack.com
• What is federated learning?research.ibm.com
• Edge AI vs Federated Learning | Complete Overviewxenonstack.com
• An Introduction to Federated Learningviso.ai
• Selective Aggregation for Low-Rank Adaptation in Federated Learningarxiv.org
• frontier model training methodologies | Alex Wa's Blogdjdumpling.github.io
• The Little Book of Deep Learningfleuret.org
• NL.pdfabehrouz.github.io
• 1902.06101arxiv.org
• MAI-Thinking-1: Building a Hill-Climbing Machinemicrosoft.ai
• Meta-Learning: Learning to Learn Fastlilianweng.github.io
• Scalable and Privacy-Preserving Federated Principal Component Analysisarxiv.org