Swarm Learning

Swarm learning (SL) is a decentralized machine learning approach designed to address privacy and scalability limitations of centralized methods like federated learning. Current research focuses on improving SL's efficiency and robustness through techniques such as blockchain integration for secure data sharing, optimization algorithms like brain storm optimization to enhance model convergence, and generative models for handling non-identically distributed data. SL's significance lies in its potential to enable collaborative model training across diverse, sensitive datasets while maintaining data privacy, with applications ranging from energy forecasting and medical image analysis to fake news detection and even amplifying collective human intelligence.