The goal of the Assisted learning protocol is to significantly expand the single-agent learning capabilities with non-private information exchanges. The above features distinguish Assisted learning from the related topic of Federated Learning, a distributed learning framework that features communication efficiency. The main idea of Federated Learning to learn a joint model using the averaging of locally learned model parameters so that the training data do not need to be transmitted. Conceptually, the main objective of Federated learning is to exploit computation and data resources of edge devices to achieve a global objective, while the goal of Assisted learning is to provide protocols for decentralized agents to assist each other with their private modeling processes. Methodologically, in Federated learning, a central controller orchestrates the learning and the optimization, and Assisted Learning provides a protocol for the agents to optimize and learn among themselves autonomously.