TL++ recovers centralized mini-batch gradients via virtual batches in split learning and adds secret sharing for cut-layer tensors, achieving 91.41% accuracy on CIFAR-10 with 13x lower communication than full-model sync.
Expert Systems (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes value-constrained credit assignment via gradient filtering and traversal learning for fully delegated AI cooperatives.
citing papers explorer
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TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems
TL++ recovers centralized mini-batch gradients via virtual batches in split learning and adds secret sharing for cut-layer tensors, achieving 91.41% accuracy on CIFAR-10 with 13x lower communication than full-model sync.
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Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
Proposes value-constrained credit assignment via gradient filtering and traversal learning for fully delegated AI cooperatives.