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Resource-aware Sub-network Allocation for Heterogeneous Decentralized Learning
System heterogeneity in computation, communication, and memory can substantially limit synchronous decentralized learning, where each training round is constrained by resource-limited nodes. Existing approaches provide limited support for jointly determining node-specific model capacities from deplo
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