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In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e.
Gradient methods for minimizing functionals
Polyak, B. T · 1963
Earlier work this paper cites.
Problems in decentralized decision making and computation
Tsitsiklis, J. N · 1984
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., and Morse, A. S · 2003
Earlier work this paper cites.
Gossip-based computation of aggregate information
Kempe, D., Dobra, A., and Gehrke, J · 2003
Earlier work this paper cites.
Fast linear iterations for distributed averaging
Xiao, L., and Boyd, S · 2004
Earlier work this paper cites.
Consensus seeking in multiagent systems under dynamically changing interaction topologies
Ren, W., and Beard, R. W · 2005
Earlier work this paper cites.
Randomized gossip algorithms
Boyd, S., Ghosh, A., Prabhakar, B., and Shah, D · 2006
Earlier work this paper cites.
Information consensus in multivehicle cooperative control
Ren, W., Beard, R. W., and Atkins, E. M · 2007
Earlier work this paper cites.
Distributed subgradient methods for multi-agent optimization
Nedic, A., and Ozdaglar, A · 2009
Earlier work this paper cites.
Quantized average consensus via dynamic coding/decoding schemes
Carli, R., Bullo, F., and Zampieri, S · 2010
Earlier work this paper cites.
Gossip consensus algorithms via quantized communication
Carli, R., Fagnani, F., Frasca, P., and Zampieri, S · 2010
Earlier work this paper cites.
Distributed estimation of Gauss-Markov random fields with one-bit quantized data
Fang, J., and Li, H · 2010
Earlier work this paper cites.
A randomized incremental subgradient method for distributed optimization in networked systems
Johansson, B., Rabi, M., and Johansson, M · 2010
Earlier work this paper cites.
Distributed consensus with limited communication data rate
Li, T., Fu, M., Xie, L., and Zhang, J.-F · 2010
Earlier work this paper cites.
Average consensus on general strongly connected digraphs
Cai, K., and Ishii, H · 2012
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Nesterov, Y · 2013
Earlier work this paper cites.
Fast convergence of stochastic gradient descent under a strong growth condition
Schmidt, M., and Roux, N. L · 2013
Earlier work this paper cites.
Average consensus on arbitrary strongly connected digraphs with time-varying topologies
Cai, K., and Ishii, H · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
Earlier work this paper cites.
EXTRA: An exact first-order algorithm for decentralized consensus optimization
Shi, W., Ling, Q., Wu, G., and Yin, W · 2015
Earlier work this paper cites.
Scalable distributed DNN training using commodity GPU cloud computing
Strom, N · 2015
Cited alongside, same era.
Linear convergence of gradient and proximal-gradient methods under the Polyak-Łojasiewicz condition
Karimi, H., Nutini, J., and Schmidt, M · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Cited alongside, same era.
On the convergence of decentralized gradient descent
Yuan, K., Ling, Q., and Yin, W · 2016
Cited alongside, same era.
QSGD: Communication-efficient SGD via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
Cited alongside, same era.
Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations
Basu, D., Data, D., Karakus, C., and Diggavi, S · 2019
Later among the works it cites.
On the linear convergence of the stochastic gradient method with constant step-size
Cevher, V., and Vũ, B. C · 2019
Later among the works it cites.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
Later among the works it cites.
Error feedback fixes SignSGD and other gradient compression schemes
Karimireddy, S. P., Rebjock, Q., Stich, S. U., and Jaggi, M · 2019
Later among the works it cites.
Decentralized deep learning with arbitrary communication compression
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Cited alongside, same era.
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Cited alongside, same era.
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