Decentralized LearningDecentralized learning is a type of Machine Learning architecture where data is distributed among several nodes. With this approach, each node has its own dataset, usually a fraction of the whole dataset, and it can train a model with its own data without the need to communicate with the other nodes. One of the most popular algorithms used in this field is Random Walk Learning (RWL). In RWL, we have a single model that is sent to a node chosen randomly among all the nodes in the network; the node will train the model for a certain number of epochs( usually one epoch) and then send it to another node chosen randomly among its neighbors which will repeat the same process.