1 citations · 1 across the 3 of their papers we have counts for
3 papers
Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted algorithm for decentralized training of machine learning models across networked agents. A cru…
Decentralized Learning over Wireless Networks with Broadcast-Based Subgraph Sampling
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considerin…
Distributed Consensus in Wireless Networks with Probabilistic Broadcast Scheduling
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
We consider distributed average consensus in a wireless network with partial communication to reduce the number of transmissions in every iteration/round. Considering the broadcast…