Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate. Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server.
Oct 14, 2022
Oct 31, 2022 · We propose a new family of network topology that is with (almost) constant degree and network-size-independent consensus rate.
To address this problem, we propose a new family of topologies,. EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate ...
Apr 3, 2024 · To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent ...
A new family of topologies is proposed, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to ...
Oct 14, 2022 · The directedness, degree, and consensus rate of various common topologies are summarized in. Table 1. The ring, grid, and torus graphs [22] ...
Communication-efficient topologies for decentralized learning with O(1) consensus rate. Z. Song, W. Li, K. Jin, L. Shi, M. Yan, W. Yin, and K. Yuan The ...
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Sep 4, 2024 · In International Conference on Machine Learning. BEER: Fast O(1/T ) rate for decentralized nonconvex optimization with communication compression.
In this study, we propose a novel topology combining both a fast consensus rate and small maximum degree called the BASE-(k + 1) GRAPH. Unlike the existing ...