Peer Learning

Peer learning, a decentralized approach to training machine learning models, focuses on enabling multiple agents or devices to collaboratively learn without relying on a central server. Current research explores various aspects, including the dynamics of distributed optimization algorithms (like distributed gradient descent) for neural networks, the application of sequence-to-sequence models in peer-to-peer settings, and techniques to enhance robustness and privacy in these systems. This field is significant for its potential to improve scalability, efficiency, and privacy in machine learning, particularly in resource-constrained environments and applications requiring data security.