Decentralized Learning

Decentralized learning focuses on collaboratively training machine learning models across multiple devices or agents without centralizing data, prioritizing privacy and scalability. Current research emphasizes developing robust algorithms that address challenges like data heterogeneity, Byzantine attacks (malicious nodes), and communication constraints, often employing techniques such as gradient tracking, secure aggregation, and adaptive learning rates within various model architectures (e.g., deep neural networks, graph neural networks). This field is significant for enabling privacy-preserving large-scale machine learning in diverse applications, from smart grids to federated learning in healthcare, and its rigorous theoretical analysis is driving improvements in both algorithm design and privacy guarantees.