[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
Multigraph fusion and classification network using graph neural network
Library to simulate a distributed learning scenario, with clusters of users that train models minimizing a local cost function, and a server that wants to minimize a global cost function. The aim of the project is to study the tradeoff between local and global accuracy.
[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
Papers related to Federated Learning in all venue (dblp)
Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.
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