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10/22/2019
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Training a machine learning model is both compute and data-intensive. Mo...
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10/02/2019
Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
In this paper, we propose coded Merkle tree (CMT), a novel hash accumula...
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06/26/2019
Coded State Machine -- Scaling State Machine Execution under Byzantine Faults
We introduce an information-theoretic framework, named Coded State Machi...
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11/08/2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address s...
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11/08/2018
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural ne...
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09/27/2018
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
Today's blockchains do not scale in a meaningful sense. As more nodes jo...
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02/08/2018
