STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
Scaffold: Stochastic controlled averaging for federated learn- ing
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Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
DeRelayL is a proposed sustainable decentralized learning paradigm where permissionless participants relay-train and share models via designed incentives, backed by theoretical analysis and simulations.
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STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
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Decentralized Learning via Random Walk with Jumps
Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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DeRelayL: Sustainable Decentralized Relay Learning
DeRelayL is a proposed sustainable decentralized learning paradigm where permissionless participants relay-train and share models via designed incentives, backed by theoretical analysis and simulations.