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Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Training a machine learning model is both compute and data-intensive. Mo...
10/22/2019 ∙ by Zhifeng Lin, et al. ∙
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Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
In this paper, we propose coded Merkle tree (CMT), a novel hash accumula...
10/02/2019 ∙ by Mingchao Yu, et al. ∙
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Coded State Machine -- Scaling State Machine Execution under Byzantine Faults
We introduce an information-theoretic framework, named Coded State Machi...
06/26/2019 ∙ by Songze Li, et al. ∙
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Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address s...
11/08/2018 ∙ by Youjie Li, et al. ∙
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GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural ne...
11/08/2018 ∙ by Mingchao Yu, et al. ∙
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PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
Today's blockchains do not scale in a meaningful sense. As more nodes jo...
09/27/2018 ∙ by Songze Li, et al. ∙
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On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast
We apply linear network coding (LNC) to broadcast a block of data packet...
02/08/2018 ∙ by Mingchao Yu, et al. ∙
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Mingchao Yu
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University of Southern California
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Australian National University
