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We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively.
Decentralized stochastic optimization and gossip algorithms with compressed communication
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Quantization and coding for decentralized lti systems
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Quantized incremental algorithms for distributed optimization
Rabbat, M. G. and Nowak, R. D. (2005) · 2005
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Quantized consensus
Kashyap, A., Basar, T., and Srikant, R. (2006) · 2006
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Distributed average consensus using probabilistic quantization
Aysal, T. C., Coates, M., and Rabbat, M. (2007) · 2007
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The tradeoffs of large scale learning
Bottou, L. and Bousquet, O. (2008) · 2008
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Distributed subgradient methods and quantization effects
Nedic, A., Olshevsky, A., Ozdaglar, A., and Tsitsiklis, J. N. (2008) · 2008
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Consensus in ad hoc wsns with noisy links–part i: Distributed estimation of deterministic signals
Schizas, I. D., Ribeiro, A., and Giannakis, G. B. (2008) · 2008
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Distributed subgradient methods for multi-agent optimization
Nedic, A. and Ozdaglar, A. (2009) · 2009
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Reining in the outliers in map-reduce clusters using mantri
Ananthanarayanan, G., Kandula, S., Greenberg, A. G., Stoica, I., Lu, Y., Saha, B., and Harris, E. (2010) · 2010
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Continuous trajectory planning of mobile sensors for informative forecasting
Choi, H.-L. and How, J. P. (2010) · 2010
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Ergodic stochastic optimization algorithms for wireless communication and networking
Ribeiro, A. (2010) · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. (2011) · 2011
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
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Large scale distributed deep networks
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Dual averaging for distributed optimization: convergence analysis and network scaling
Duchi, J. C., Agarwal, A., and Wainwright, M. J. (2012) · 2012
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Push-sum distributed dual averaging for convex optimization
Tsianos, K. I., Lawlor, S., and Rabbat, M. G. (2012) · 2012
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Effective straggler mitigation: Attack of the clones
Ananthanarayanan, G., Ghodsi, A., Shenker, S., and Stoica, I. (2013) · 2013
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The tail at scale
Dean, J. and Barroso, L. A. (2013) · 2013
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Fast distributed gradient methods
Jakovetic, D., Xavier, J., and Moura, J. M. (2014) · 2014
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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) · 2014
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On the linear convergence of the admm in decentralized consensus optimization
Shi, W., Ling, Q., Yuan, K., Wu, G., and Yin, W. (2014) · 2014
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
Seaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L. (2017) · 2017
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Decentralized consensus optimization with asynchrony and delays
Wu, T., Yuan, K., Ling, Q., Yin, W., and Sayed, A. H. (2017) · 2017
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Polynomial codes: an optimal design for high-dimensional coded matrix multiplication
Yu, Q., Maddah-Ali, M. A., and Avestimehr, A. S. (2017) · 2017
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signsgd: Compressed optimisation for non-convex problems
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A. (2018) · 2018
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Accelerating the convergence rates of distributed subgradient methods with adaptive quantization
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Efficient task replication for fast response times in parallel computation
Wang, D., Joshi, G., and Wornell, G. (2014) · 2014
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Multi-agent distributed optimization via inexact consensus admm
Chang, T.-H., Hong, M., and Wang, X. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Design and analysis of distributed averaging with quantized communication
El Chamie, M., Liu, J., and Başar, T. (2016) · 2016
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Path planning in gps-denied environments via collective intelligence of distributed sensor networks
Jha, D. K., Chattopadhyay, P., Sarkar, S., and Ray, A. (2016) · 2016
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Dsa: Decentralized double stochastic averaging gradient algorithm
Mokhtari, A. and Ribeiro, A. (2016) · 2016
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Doan, T. T., Maguluri, S. T., and Romberg, J. (2018) · 2018
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Slow and stale gradients can win the race: Error-runtime trade-offs in distributed sgd
Dutta, S., Joshi, G., Ghosh, S., Dube, P., and Nagpurkar, P. (2018) · 2018
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Hong, M., Lee, J. D., and Razaviyayn, M. (2018) · 2018
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Asynchronous decentralized accelerated stochastic gradient descent
Lan, G. and Zhou, Y. (2018) · 2018
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Distributed quantized weight-balancing and average consensus over digraphs
Lee, C.-S., Michelusi, N., and Scutari, G. (2018a) · 2018
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Finite rate quantized distributed optimization with geometric convergence
Lee, C.-S., Michelusi, N., and Scutari, G. (2018b) · 2018
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The landscape of empirical risk for nonconvex losses
Mei, S., Bai, Y., Montanari, A., et al. (2018) · 2018
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Quantized decentralized consensus optimization
Reisizadeh, A., Mokhtari, A., Hassani, H., and Pedarsani, R. (2018) · 2018
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Optimal algorithms for non-smooth distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Massoulié, L., and Lee, Y. T. (2018) · 2018
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Distributed nonconvex constrained optimization over time-varying digraphs
Scutari, G. and Sun, Y. (2018) · 2018
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Sun, H. and Hong, M. (2018) · 2018
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A dual approach for optimal algorithms in distributed optimization over networks
Uribe, C. A., Lee, S., Gasnikov, A., and Nedić, A. (2018) · 2018
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On nonconvex decentralized gradient descent
Zeng, J. and Yin, W. (2018) · 2018
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Compressed distributed gradient descent: Communication-efficient consensus over networks
Zhang, X., Liu, J., Zhu, Z., and Bentley, E. S. (2018) · 2018
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Anytime minibatch: Exploiting stragglers in online distributed optimization
Ferdinand, N., Al-Lawati, H., Draper, S., and Nokleby, M. (2019) · 2019
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