Federico Ghiglione
Random Walking Spiders for Decentralized Learning at Edge Networks.
Rel. Eros Gian Alessandro Pasero, Erdem Koyuncu. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
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.
In this way, the model can learn from different nodes during the learning process, but it faces some challenges, such as scenarios where the data distribution among the nodes is heterogenous, leading to accuracy issues because the accuracy could depend on the path chosen from the first node to the last one
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