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Decentralized adaptive clustering of deep nets is beneficial for client collaboration
[article]
2022
arXiv
pre-print
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. We study both covariate and label shift, and our contribution is an algorithm which for each client finds beneficial collaborations based on a similarity estimate for the local task. Our method does not rely on hyperparameters which are hard to
arXiv:2206.08839v2
fatcat:szvu5ey6afhopa2w2cnj27krki