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Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge.
A dual algorithm for the solution of nonlinear variational problems via finite element approximation
Daniel Gabay and Bertrand Mercier · 1976
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Computing a trust region step
Jorge Moré and Danny Sorensen · 1983
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, and Eric Chu · 2011
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Hankel matrix rank minimization with applications to system identification and realization
Maryam Fazel, Ting Kei Pong, Defeng Sun, and Paul Tseng · 2013
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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Deep learning with elastic averaging SGD
Sixin Zhang, Anna Choromanska, and Yann LeCun · 2015
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Fast ADMM algorithm for distributed optimization with adaptive penalty
Changkyu Song, Sejong Yoon, and Vladimir Pavlovic · 2016
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Asynchronous stochastic gradient descent with delay compensation
Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, and Tie-Yan Liu · 2017
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Robust federated learning using ADMM in the presence of data falsifying byzantines
Qunwei Li, Bhavya Kailkhura, Ryan Goldhahn, Priyadip Ray, and Pramod K. Varshney · 2017
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A survey on mobile edge computing: The communication perspective
Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B Letaief · 2017
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Mobile edge computing: A survey on architecture and computation offloading
Pavel Mach and Zdenek Becvar · 2017
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Lag: Lazily aggregated gradient for communication-efficient distributed learning
Tianyi Chen, Georgios B. Giannakis, Tao Sun, and Wotao Yin · 2018
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Improving the privacy and accuracy of ADMM-based distributed algorithms
Xueru Zhang, Mohammad Mahdi Khalili, and Mingyan Liu · 2018
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A stackelberg game approach to proactive caching in large-scale mobile edge networks
Zijie Zheng, Lingyang Song, Zhu Han, Geoffrey Ye Li, and H Vincent Poor · 2018
Cited alongside, same era.
MIMO over-the-air computation for high-mobility multimodal sensing
Guangxu Zhu and Kaibin Huang · 2018
Cited alongside, same era.
Distributed federated learning for ultra-reliable low-latency vehicular communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad, and Mérouane Debbah · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
Sebastian U. Stich · 2019
Cited alongside, same era.
Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning
Don’t use large mini-batches, use local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, and Martin Jaggi · 2020
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Federated nonconvex sparse learning
Qianqian Tong, Guannan Liang, Tan Zhu, and Jinbo Bi · 2020
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L-FGADMM: Layer-wise federated group ADMM for communication efficient decentralized deep learning
Anis Elgabli, Jihong Park, Sabbir Ahmed, and Mehdi Bennis · 2020
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Communication efficient distributed learning with censored, quantized, and generalized group admm
Chaouki Ben Issaid, Anis Elgabli, Jihong Park, Mehdi Bennis, and Mérouane Debbah · 2020
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Federated learning via over-the-air computation
Kai Yang, Tao Jiang, Yuanming Shi, and Zhi Ding · 2020
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Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
Cited alongside, same era.
Distributed reinforcement learning with ADMM-RL
Peter Graf, Jennifer Annoni, Christopher Bay, Dave Biagioni, Devon Sigler, Monte Lunacek, and Wesley Jones · 2019
Cited alongside, same era.
DP-ADMM: ADMM-based distributed learning with differential privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo, Eric Chan-Tin, and Yanmin Gong · 2019
Cited alongside, same era.
Greedy projected gradient-Newton method for sparse logistic regression
Rui Wang, Naihua Xiu, and Chao Zhang · 2019
Cited alongside, same era.
Federated learning meets blockchain at 6g edge: A drone-assisted networking for disaster response
Shiva Raj Pokhrel · 2020
Cited alongside, same era.
Federated learning in vehicular networks
Ahmet M Elbir, Burak Soner, and Sinem Coleri · 2020
Cited alongside, same era.
Communication-efficient massive UAV online path control: Federated learning meets mean-field game theory
Hamid Shiri, Jihong Park, and Mehdi Bennis · 2020
Later among the works it cites.
Federated learning in vehicular networks: opportunities and solutions
Jason Posner, Lewis Tseng, Moayad Aloqaily, and Yaser Jararweh · 2021
Closest in time.
Federated learning and wireless communications
Zhijin Qin, Geoffrey Ye Li, and Hao Ye · 2021
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Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms
Jianyu Wang and Gauri Joshi · 2021
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Decentralized learning with lazy and approximate dual gradients
Yanli Liu, Yuejiao Sun, and Wotao Yin · 2021
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Inexact-ADMM based federated meta-learning for fast and continual edge learning
Sheng Yue, Ju Ren, Jiang Xin, Sen Lin, and Junshan Zhang · 2021
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Differentially private federated learning via inexact ADMM
Minseok Ryu and Kibaek Kim · 2021
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Decentralized federated learning with unreliable communications
Hao Ye, Le Liang, and Geoffrey Li · 2021
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