Sheaf-FMTL learns projection maps between heterogeneous client models in decentralized federated learning, saving communication, but its convergence proof is incomplete and its objective degenerates to independent training.
Sheaves provide a natural way to ensure consistency between local (client-specific) and global (network-wide) information
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Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
Sheaf-FMTL learns projection maps between heterogeneous client models in decentralized federated learning, saving communication, but its convergence proof is incomplete and its objective degenerates to independent training.