Distributed under Creative Commons Cc-by 4.0 Mlitb: Machine Learning in the Browser
By Edward Meeds, J Nl, S. Ventura, Remco Hendriks, Al Faraby, Magiel Bruntink, Max Welling
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Cong Xie
- Sai Praneeth Karimireddy
- Oluwasanmi Koyejo
- Lie He
- Indranil Gupta
- Martin Jaggi
- Jun Zhu
- Jianfei Chen
Citations in the corpus
- Zeno++: Robust Fully Asynchronous SGD (2019, background): “istributed settings has attracted increasing attention in the machine-learning community [1, 2, 3, 5, 13, 14, 15, 16]. In certain distributed settings,suchasfederatedlearning[7,8]andvolunteercomputing[10,11],theworkers/contributors can be unreliable. The computation nodes can be personal mobile devi”
- Zeno: Byzantine-suspicious stochastic gradient descent (2018, background): “In certain distributed settings, such as federated learning [9, 10] and volunteer computing [14, 16], the workers/contributors can be unreliable.”
- Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance (2018, background): “In volunteer computing (Meeds et al., 2015; Miura & Harada, 2015), it is reasonable for the coordinator to assign disjoint tasks/datasets to different workers.”
- WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, background): “…Cache; MPI; NUMA Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the…”
- Zeno++: robust asynchronous SGD with arbitrary number of Byzantine workers (2019, background): “In certain distributed settings, such as federated learning [7, 8] and volunteer computing [10, 11], the workers/contributors can be unreliable.”
- Byzantine-Robust Learning on Heterogeneous Datasets via Resampling (2020, background): “Typical examples of this is distributed training of neural networks on public cloud, or volunteer computing Meeds et al. (2015); Miura and Harada (2015).”