Decentralized Manner

Decentralized approaches aim to perform computations and learning across multiple entities without relying on a central server, prioritizing data privacy and robustness. Current research focuses on developing algorithms and architectures for decentralized federated learning, multi-agent systems, and distributed consensus, often employing techniques like gradient pushing, workload balancing, and secure aggregation protocols. This area is significant due to its potential to enable collaborative machine learning on sensitive data while mitigating single points of failure and enhancing scalability, with applications ranging from healthcare to autonomous systems. The development of efficient and privacy-preserving decentralized methods is a key challenge driving ongoing research.