CMPS 4760/6760 Spring 2021, Paper Reading List
Distributed
Optimization and Control
- Rate
control for communication networks: shadow prices, proportional
fairness and stability, F. Kelly, et al. (Journal of the
Operational Research Society 1998)
-
Fast
linear iterations for distributed averaging, L. Xiao and S. Boyd
(CDC 2003)
- Randomized
gossip
algorithms, S. Boyd, et al. (IEEE Transactions on Information
Theory, 2006)
- Spread of
(mis)information in social networks, D. Acemoglu, et al. (Games
and Economic Behavior, 2010)
- Distributed
Alternating
Direction Method of Multipliers, E. Wei and A. Ozdaglar (CDC
2012)
- Dynamics
at
the boundary of game theory and distributed computing, A. D.
Jaggard, et al. (TEAC, 2018)
- Convergence
rate of distributed subgradient methods under communication delays,
T. T. Doan, et al. (ACC 2018)
- Network Topology and
Communication-Computation Tradeoffs in Decentralized Optimization,
A. Nedic, et al. (Proceedings of the IEEE, 2018)
- Byzantine-resilient
multi-agent optimization, L. Su and N. H. Vaidya (IEEE TAC 2020)
Distributed
Machine Learning
- Coordinated
reinforcement learning, C. Guestrin, et al. (ICML 2002)
- Learning
to Communicate with Deep Multi-Agent Reinforcement Learning, J.
N. Foerster, et al. (NIPS 2016)
- Fully
Decentralized Multi-Agent Reinforcement Learning with Networked
Agents, K. Zhang, et al. (ICML 2018)
- Agnostic
Federated Learning, M. Mohri, et al. (ICML 2019)
- Fair Resource
Allocation in Federated Learning, T. Li, et al. (ICLR 2020)
- How
To Backdoor Federated Learning, E. Bagdasaryan, et al. (AISTATS
2020)
- The
Non-IID Data Quagmire of Decentralized Machine Learning, K.
Hsieh, et al. (ICML 2020)
Blockchains
- Analysis of the
Blockchain Protocol in Asynchronous Networks, R. Pass, et al.
(EUROCRYPT 2017)
- Casper the Friendly
Finality Gadget, V. Buterin and V. Griffith (2017)
- Publish
or
Perish: A Backward-Compatible Defense against Selfish Mining in
Bitcoin, R. Zhang and B. Preneel (RSA 2017)
- PHANTOM, GHOSTDAG:
Two Scalable BlockDAG Protocols, Y. Sompolinsky and A. Zohar
(2018)
- ALGORAND AGREEMENT:
Super Fast and Partition Resilient Byzantine Agreement, J. Chen
et al. (2018)
- Majority
is
not Enough: Bitcoin Mining is Vulnerable, I. Eyal and E. G.
Sirer (Communications of the ACM, 2018)
- Blockchains
from a distributed computing perspective, M. Herlihy
(Communications of the ACM 2019)
- Adding concurrency to
smart contracts, T. Dickerson, et al. (Distributed Computing,
2020)
Cloud
Computing
- The Google File
System, S. Ghemawat, et al. (SOSP 2003)
- Bigtable: a
distributed storage system for structured data, F. Chang, et al.
(OSDI 2006)
- The
Hadoop Distributed File System, K. Shavachko, et al. (MSST 2010)
- Improving
MapReduce
Performance in Heterogeneous Environments, M. Zaharia, et al.
(OSDI 2008)
- Spark:
Cluster
Computing with Working Sets, M. Zaharia, et al. (HotCloud 2010)
- Mesos:
A Platform for Fine-Grained Resource Sharing in the Data Center,
B. Hindman, et al. (NSDI 2011)
- Apache Hadoop
YARN: Yet Another Resource Negotiator, V. K. Vavilapallih, et
al. (SoCC 2013)
- Sparrow:
Distributed, Low Latency Scheduling, K. Ousterhout, et al. (SOSP
2013)
- GraphX: Graph
Processing in a Distributed Dataflow Framework, J. Gonzalez, et
al. (OSDI 2014)
- Drizzle: Fast and
Adaptable Stream Processing at Scale, S. Venkataraman, et al.
(SOSP 17)
Large-Scale
Machine Learning
- Scaling Distributed
Machine Learning with the Parameter Server, M. Li, et al. (OSDI
2014)
- Project Adam: Building
an Efficient and Scalable Deep Learning Training System, T.
Chilimbi, et al. (OSDI 2014)
- TensorFlow:
A
System for Large-Scale Machine Learning, M. Abadi, et al. (OSDI
2016)
- Clipper:
A
Low-Latency Online Prediction Serving System, D. Crankshaw, et
al. (NSDI 2017)
- Gaia:
Geo-Distributed
Machine Learning Approaching LAN Speeds, K. Hsieh, et al. (NSDI
2017)
- Communication-Efficient
Learning
of Deep Networks from Decentralized Data, H. B. McMahan (AISTATS
2017)
- Gradient
Coding:
Avoiding Stragglers in Distributed Learning, R. Tandon, et al.
(ICML 2017)
- Speeding Up
Distributed Machine Learning Using Codes, K. Lee, et al. (IEEE
Transactions on Information Theory, 2018)
- SLAQ:
Quality-Driven
Scheduling for Distributed Machine Learning, H. Zhang, et al.
(SoCC 2017)
- Machine
Learning with Adversaries: Byzantine Tolerant Gradient Descent,
P. Blanchard, et al. (NIPS 2017)
- Value Propagation for
Decentralized Networked Deep Multi-agent Reinforcement Learning,
C. Qu, et al., (2019)
Internet
of Things (IoT)