Fetching the paper…
Reading the bibliography…
Federated averaging (FedAvg) is a communication efficient algorithm for the distributed training with an enormous number of clients.
H. Robbins and S. Monro, “A stochastic approximation method,” Annals of Mathematical Statistics
1951
Earlier work this paper cites.
B. T. Polyak, “Gradient methods for minimizing functionals,” Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki
1963
Earlier work this paper cites.
S. Lojasiewicz, “A topological property of real analytic subsets,” Coll. du CNRS, Les équations aux dérivées partielles
1963
Earlier work this paper cites.
B. T. Polyak, “Some methods of speeding up the convergence of iteration methods,” Ussr Computational Mathematics and Mathematical Physics
1964
Earlier work this paper cites.
S. Boyd, P. Diaconis, and L. Xiao, “Fastest mixing markov chain on a graph,” SIAM review
2004
Earlier work this paper cites.
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Gossip algorithms: Design, analysis and applications,” in INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE
2005
Earlier work this paper cites.
R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE
2007
Earlier work this paper cites.
L. Schenato and G. Gamba, “A distributed consensus protocol for clock synchronization in wireless sensor network,” in 2007 46th ieee conference on decision and control
2007
Earlier work this paper cites.
T. C. Aysal, M. E. Yildiz, A. D. Sarwate, and A. Scaglione, “Broadcast gossip algorithms for consensus,” IEEE Transactions on Signal processing
2009
Earlier work this paper cites.
A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Transactions on Automatic Control
2009
Earlier work this paper cites.
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro, “Robust stochastic approximation approach to stochastic programming,” SIAM Journal on optimization
2009
Earlier work this paper cites.
M. Zinkevich, M. Weimer, L. Li, and A. J. Smola, “Parallelized stochastic gradient descent,” in Advances in neural information processing systems
2010
Earlier work this paper cites.
I. Matei and J. S. Baras, “Performance evaluation of the consensus-based distributed subgradient method under random communication topologies,” IEEE Journal of Selected Topics in Signal Processing
2011
Earlier work this paper cites.
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, “Pegasos: Primal estimated sub-gradient solver for svm,” Mathematical programming
2011
Earlier work this paper cites.
A. I. Chen and A. Ozdaglar, “A fast distributed proximal-gradient method,” in Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on
2012
Earlier work this paper cites.
I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning
2013
Earlier work this paper cites.
S. Ghadimi and G. Lan, “Stochastic first-and zeroth-order methods for nonconvex stochastic programming,” SIAM Journal on Optimization
2013
Earlier work this paper cites.
D. Jakovetić, J. Xavier, and J. M. Moura, “Fast distributed gradient methods,” IEEE Transactions on Automatic Control
2014
Cited alongside, same era.
M. Li, D. G. Andersen, A. J. Smola, and K. Yu, “Communication efficient distributed machine learning with the parameter server,” in Advances in Neural Information Processing Systems
2014
Cited alongside, same era.
K. Yuan, Q. Ling, and W. Yin, “On the convergence of decentralized gradient descent,” SIAM Journal on Optimization
2016
Cited alongside, same era.
B. Sirb and X. Ye, “Consensus optimization with delayed and stochastic gradients on decentralized networks,” in Big Data (Big Data), 2016 IEEE International Conference on
2016
Cited alongside, same era.
H. Karimi, J. Nutini, and M. Schmidt, “Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2019
Later among the works it cites.
J. Sun, T. Chen, G. Giannakis, and Z. Yang, “Communication-efficient distributed learning via lazily aggregated quantized gradients,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smithy, “Feddane: A federated newton-type method,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola, “Stochastic variance reduction for nonconvex optimization,” in International conference on machine learning
2016
Cited alongside, same era.
H. B. Mcmahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, “Communication-efficient learning of deep networks from decentralized data,” pp. 1273–1282, 2017
2017
Cited alongside, same era.
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu, “Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
A. Lalitha, S. Shekhar, T. Javidi, and F. Koushanfar, “Fully decentralized federated learning,” in Third workshop on Bayesian Deep Learning (NeurIPS)
2018
Cited alongside, same era.
T. Chen, G. Giannakis, T. Sun, and W. Yin, “Lag: Lazily aggregated gradient for communication-efficient distributed learning,” in Advances in Neural Information Processing Systems
2018
Cited alongside, same era.
J. Zeng and W. Yin, “On nonconvex decentralized gradient descent,” IEEE Transactions on Signal Processing
2018
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions.,” arXiv: Learning
2019
Later among the works it cites.
T. Sun, P. Yin, D. Li, C. Huang, L. Guan, and H. Jiang, “Non-ergodic convergence analysis of heavy-ball algorithms,” in Proceedings of the AAAI Conference on Artificial Intelligence
2019
Later among the works it cites.
R. Xin and U. A. Khan, “Distributed heavy-ball: A generalization and acceleration of first-order methods with gradient tracking,” IEEE Transactions on Automatic Control
2019
Later among the works it cites.
Morgan & Claypool Publishers, 2019
Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu, Federated learning · 2019
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of the 1 st Adaptive & Multitask Learning Workshop, Long Beach, California
2019
Later among the works it cites.
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, “Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,” In Annual Network and Distributed System Security Symposium (NDSS 2019)
2019
Later among the works it cites.
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-IID data,” in International Conference on Learning Representations
2020
Later among the works it cites.
G. Lan, S. Lee, and Y. Zhou, “Communication-efficient algorithms for decentralized and stochastic optimization,” Mathematical Programming
2020
Later among the works it cites.
H. Xing, O. Simeone, and S. Bi, “Decentralized federated learning via SGD over wireless D2D networks,” in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2020
Later among the works it cites.
S. Magnússon, H. Shokri-Ghadikolaei, and N. Li, “On maintaining linear convergence of distributed learning and optimization under limited communication,” IEEE Transactions on Signal Processing
2020
Later among the works it cites.
S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan, “Adaptive federated optimization,” in International Conference on Learning Representations
2021
Closest in time.
H. B. McMahan et al
2021
Closest in time.