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Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.
Towards federated learning at scale: System design
Bonawitz, K.; Eichner, H.; Grieskamp, W.; Huba, D.; Ingerman, A.; Ivanov, V.; Kiddon, C.; Kone c ˇ \check{\text{c}} nỳ, J.; Mazzocchi, S.; McMahan, H. B.; et al. 2019 · 1902
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Robust and communication-efficient federated learning from non-iid data
Sattler, F.; Wiedemann, S.; Müller, K.-R.; and Samek, W. 2019 · 1903
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Semi-cyclic stochastic gradient descent
Eichner, H.; Koren, T.; McMahan, H. B.; Srebro, N.; and Talwar, K. 2019 · 1904
Earlier work this paper cites.
MATCHA: Speeding up decentralized SGD via matching decomposition sampling
Wang, J.; Sahu, A. K.; Yang, Z.; Joshi, G.; and Kar, S. 2019a · 1905
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Hybrid-FL: Cooperative learning mechanism using non-iid Data in wireless networks
Yoshida, N.; Nishio, T.; Morikura, M.; Yamamoto, K.; and Yonetani, R. 2019 · 1905
Earlier work this paper cites.
Robust federated learning in a heterogeneous environment
Ghosh, A.; Hong, J.; Yin, D.; and Ramchandran, K. 2019 · 1906
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Fedmd: Heterogenous federated learning via model distillation
Li, D.; and Wang, J. 2019 · 1910
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Overcoming Forgetting in Federated Learning on Non-IID Data
Shoham, N.; Avidor, T.; Keren, A.; Israel, N.; Benditkis, D.; Mor-Yosef, L.; and Zeitak, I. 2019 · 1910
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Distributed Optimization over Block-Cyclic Data
Ding, Y.; Niu, C.; Yan, Y.; Zheng, Z.; Wu, F.; Chen, G.; Tang, S.; and Jia, R. 2020 · 2002
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Luo, S.; Chen, X.; Wu, Q.; Zhou, Z.; and Yu, S. 2020 · 2002
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Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
So, J.; Guler, B.; and Avestimehr, A. S. 2020 · 2002
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Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Briggs, C.; Fan, Z.; Andras, P.; and Andras, P. 2020 · 2004
Earlier work this paper cites.
Multi-Center Federated Learning
Xie, M.; Long, G.; Shen, T.; Zhou, T.; Wang, X.; and Jiang, J. 2020 · 2005
Cited alongside, same era.
An Efficient Framework for Clustered Federated Learning
Ghosh, A.; Chung, J.; Yin, D.; and Ramchandran, K. 2020 · 2006
Cited alongside, same era.
Group knowledge transfer: Collaborative training of large cnns on the edge
He, C.; Avestimehr, S.; and Annavaram, M. 2020 · 2007
Cited alongside, same era.
FedML: A Research Library and Benchmark for Federated Machine Learning
He, C.; Li, S.; So, J.; Zhang, M.; Wang, H.; Wang, X.; Vepakomma, P.; Singh, A.; Qiu, H.; Shen, L.; Zhao, P.; Kang, Y.; Liu, Y.; Raskar, R.; Yang, Q.; Annavaram, M.; and Avestimehr, S. 2020 · 2007
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
Later among the works it cites.
Gossip learning as a decentralized alternative to federated learning
Hegedűs, I.; Danner, G.; and Jelasity, M. 2019 · 2019
Later among the works it cites.
Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T.; and Yonetani, R. 2019 · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
Parisi, G. I.; Kemker, R.; Part, J. L.; Kanan, C.; and Wermter, S. 2019 · 2019
Later among the works it cites.
Multi-objective evolutionary federated learning
Zhu, H.; and Jin, Y. 2019 · 2019
Later among the works it cites.
Hierarchical federated learning across heterogeneous cellular networks
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Kone c ˇ \check{\text{c}} nỳ, J.; McMahan, H. B.; Yu, F. X.; Richtárik, P.; Suresh, A. T.; and Bacon, D. 2016b · 2016
Cited alongside, same era.
Federated multi-task learning
Smith, V.; Chiang, C.-K.; Sanjabi, M.; and Talwalkar, A. S. 2017 · 2017
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
Caldas, S.; Kone c ˇ \check{\text{c}} nỳ, J.; McMahan, H. B.; and Talwalkar, A. 2018 · 2018
Cited alongside, same era.
Jeong, E.; Oh, S.; Kim, H.; Park, J.; Bennis, M.; and Kim, S.-L. 2018 · 2018
Cited alongside, same era.
Pipe-sgd: A decentralized pipelined sgd framework for distributed deep net training
Li, Y.; Yu, M.; Li, S.; Avestimehr, S.; Kim, N. S.; and Schwing, A. 2018 · 2018
Cited alongside, same era.
Don’t Use Large Mini-Batches, Use Local SGD
Lin, T.; Stich, S. U.; Patel, K. K.; and Jaggi, M. 2018 · 2018
Cited alongside, same era.
On the convergence of federated optimization in heterogeneous networks
Sahu, A. K.; Li, T.; Sanjabi, M.; Zaheer, M.; Talwalkar, A.; and Smith, V. 2018 · 2018
Cited alongside, same era.
Federated optimization: Distributed machine learning for on-device intelligence
Kone c ˇ \check{\text{c}} nỳ, J.; McMahan, H. B.; Ramage, D.; and Richtárik, P. 2016a
Cited in the paper.
Abad, M. S. H.; Ozfatura, E.; Gunduz, D.; and Ercetin, O. 2020 · 2020
Closest in time.
Self-balancing federated learning with global imbalanced data in mobile systems
Duan, M.; Liu, D.; Chen, X.; Liu, R.; Tan, Y.; and Liang, L. 2020 · 2020
Closest in time.
On the convergence of fedavg on non-iid data
Li, X.; Huang, K.; Yang, W.; Wang, S.; and Zhang, Z. 2020 · 2020
Closest in time.
Client-edge-cloud hierarchical federated learning
Liu, L.; Zhang, J.; Song, S.; and Letaief, K. B. 2020 · 2020
Closest in time.
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Sattler, F.; Müller, K.-R.; and Samek, W. 2020 · 2020
Closest in time.
Federated Continual Learning with Weighted Inter-client Transfer
Yoon, J.; Jeong, W.; Lee, G.; Yang, E.; and Hwang, S. J. 2020 · 2020
Closest in time.