DICE defines and approximates multi-hop data influence in decentralized learning, showing that influence is shaped by data, topology, and loss curvature.
Let G = (V, E) denote a decentralized communication topology, where V is the set of n nodes and E is the set of edges
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DICE: Data Influence Cascade in Decentralized Learning
DICE defines and approximates multi-hop data influence in decentralized learning, showing that influence is shaped by data, topology, and loss curvature.