Search Results for author:
Found 2 papers, 1 papers with code
EFFGAN: Ensembles of fine-tuned federated GANs
This is an even harder problem when the data is decentralized over several clients in a federated learning setup, as problems such as client drift and non-iid data make it hard for federated averaging to converge.
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
