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FedNL: Making Newton-Type Methods Applicable to Federated Learning
[article]
2022
arXiv
pre-print
Inspired by recent work of Islamov et al (2021), we propose a family of Federated Newton Learn (FedNL) methods, which we believe is a marked step in the direction of making second-order methods applicable to FL. In contrast to the aforementioned work, FedNL employs a different Hessian learning technique which i) enhances privacy as it does not rely on the training data to be revealed to the coordinating server, ii) makes it applicable beyond generalized linear models, and iii) provably works
arXiv:2106.02969v2
fatcat:fzg2w465qzdhzftjusfrsu3zhi