Spread: Decentralized Model Aggregation for Scalable Federated Learning

Author:

Hu Chuang1,Liang Huang Huang2,Han Xiao Ming2,Liu Bo An2,Cheng Da Zhao2,Wang Dan3

Affiliation:

1. Computer Science, Wuhan University, China

2. Wuhan University, China

3. Hong Kong Polytechnic University, Hong Kong

Publisher

ACM

Reference50 articles.

1. CIFAR-10: KNN-Based Ensemble of Classifiers

2. Hamed Hassani Ali Jadbabaie Ramtin Pedarsani Amirhossein Reisizadeh, Aryan Mokhtari. Aug. , 2020 . FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization . In Proc.of AISTATS’20 . virtual. Hamed Hassani Ali Jadbabaie Ramtin Pedarsani Amirhossein Reisizadeh, Aryan Mokhtari. Aug., 2020. FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. In Proc.of AISTATS’20. virtual.

3. Lukas Balles Javier Romero and Philipp Hennig. 2016. Coupling adaptive batch sizes with learning rates. arXiv preprint arXiv:1612.05086(2016). Lukas Balles Javier Romero and Philipp Hennig. 2016. Coupling adaptive batch sizes with learning rates. arXiv preprint arXiv:1612.05086(2016).

4. Keith Bonawitz Hubert Eichner Wolfgang Grieskamp Dzmitry Huba Alex Ingerman Vladimir Ivanov Chloe Kiddon Jakub Konecny Stefano Mazzocchi H Brendan McMahan 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046(2019). Keith Bonawitz Hubert Eichner Wolfgang Grieskamp Dzmitry Huba Alex Ingerman Vladimir Ivanov Chloe Kiddon Jakub Konecny Stefano Mazzocchi H Brendan McMahan 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046(2019).

5. Large-Scale Machine Learning with Stochastic Gradient Descent

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