MCE-PL lets each agent learn a personalized binary mask on a shared fixed random network, transmitting only masks, and the paper's theoretical DSLTH proof is intended to justify this design.
Can decentralized algorithms outperform centrali zed algorithms? A case study for decentralized parallel stocha stic gradient descent,
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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity
MCE-PL lets each agent learn a personalized binary mask on a shared fixed random network, transmitting only masks, and the paper's theoretical DSLTH proof is intended to justify this design.