Publications (16)
The Benefit of Limited Feedback to Generation-Based Random Linear Network Coding in Wireless Broadcast
Mingchao Yu, Parastoo Sadeghi, Alex Sprintson
Random linear network coding (RLNC) is asymptotically throughput optimal in the wireless broadcast of a block of packets from a sender to a set of receivers, but suffers from heavy…
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Zhifeng Lin, Krishna Giri Narra, Mingchao Yu +2
Training a machine learning model is both compute and data-intensive. Most of the model training is performed on high performance compute nodes and the training data is stored near…
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
Songze Li, Mingchao Yu, Chien-Sheng Yang +3
Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current…
Bitcoin Staking
Xinshu Dong, Orfeas Stefanos Thyfronitis Litos, Ertem Nusret Tas +4
The idea of security sharing goes back to Nakamoto's introduction of merge mining, a technique that enables Bitcoin miners to reuse their hash power to bootstrap and secure other P…
Coded State Machine -- Scaling State Machine Execution under Byzantine Faults
Songze Li, Saeid Sahraei, Mingchao Yu +3
We introduce an information-theoretic framework, named Coded State Machine (CSM), to securely and efficiently execute multiple state machines on untrusted network nodes, some of wh…
Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
Mingchao Yu, Saeid Sahraei, Songze Li +3
In this paper, we propose coded Merkle tree (CMT), a novel hash accumulator that offers a constant-cost protection against data availability attacks in blockchains, even if the maj…
On Deterministic Linear Network Coded Broadcast and Its Relation to Matroid Theory
Mingchao Yu, Parastoo Sadeghi, Neda Aboutorab
Deterministic linear network coding (DLNC) is an important family of network coding techniques for wireless packet broadcast. In this paper, we show that DLNC is strongly related t…
On Throughput and Decoding Delay Performance of Instantly Decodable Network Coding
Mingchao Yu, Parastoo Sadeghi, Neda Aboutorab
In this paper, a comprehensive study of packet-based instantly decodable network coding (IDNC) for single-hop wireless broadcast is presented. The optimal IDNC solution in terms of…
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra +6
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To allev…
Approximating Throughput and Packet Decoding Delay in Linear Network Coded Wireless Broadcast
Mingchao Yu, Parastoo Sadeghi
In this paper, we study a wireless packet broadcast system that uses linear network coding (LNC) to help receivers recover data packets that are missing due to packet erasures. We…
Instantly Decodable versus Random Linear Network Coding: A Comparative Framework for Throughput and Decoding Delay Performance
Parastoo Sadeghi, Mingchao Yu
This paper studies the tension between throughput and decoding delay performance of two widely-used network coding schemes: random linear network coding (RLNC) and instantly decoda…
On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast
Mingchao Yu, Alex Sprintson, Parastoo Sadeghi
We apply linear network coding (LNC) to broadcast a block of data packets from one sender to a set of receivers via lossy wireless channels, assuming each receiver already possesse…
Performance Characterization and Transmission Schemes for Instantly Decodable Network Coding in Wireless Broadcast
Mingchao Yu, Parastoo Sadeghi, Neda Aboutorab
We consider broadcasting a block of packets to multiple wireless receivers under random packet erasures using instantly decodable network coding (IDNC). The sender first broadcasts…
From Instantly Decodable to Random Linear Network Coding
Mingchao Yu, Neda Aboutorab, Parastoo Sadeghi
Our primary goal in this paper is to traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network cod…
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li, Mingchao Yu, Songze Li +3
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical dis…
On Minimizing the Average Packet Decoding Delay in Wireless Network Coded Broadcast
Mingchao Yu, Alex Sprintson, Parastoo Sadeghi
We consider a setting in which a sender wishes to broadcast a block of K data packets to a set of wireless receivers, where each of the receivers has a subset of the data packets a…