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Found 4 papers, 2 papers with code
A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models
Decentralized solutions such as blockchain have been proposed to tackle these issues, but they often struggle when dealing with large-scale models, leading to time-consuming inference and inefficient training verification.
DepthFL: Depthwise Federated Learning for Heterogeneous Clients
Federated learning is for training a global model without collecting private local data from clients.
ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference
That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model.
Distributed, Parallel, and Cluster Computing
Ethanos: Lightweight Bootstrapping for Ethereum
Our experimental result shows that ethanos can reduce the size of the account state by half, which, if combined with removing old transactions, may reduce the storage size for bootstrapping to around 1GB.
