Decentralized Aggregation

Decentralized aggregation focuses on efficiently and securely combining data or model updates from multiple distributed sources without relying on a central server, addressing scalability and privacy concerns in machine learning. Current research emphasizes developing robust aggregation protocols that handle communication bottlenecks, client dropout, and adversarial attacks, often employing techniques like secret sharing, low-rank adaptation, and hybrid centralized-decentralized approaches. These advancements are crucial for enabling privacy-preserving federated learning and other distributed applications, improving efficiency and resilience in large-scale machine learning systems.