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.
Ensemble distillation for robust model fusion in federated learning
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FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
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.
citing papers explorer
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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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From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
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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.