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To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its neighbors in a decentralized communication network.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Distributed subgradient methods for multi-agent optimization
Nedic, A. and Ozdaglar, A · 2009
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Large-scale machine learning with stochastic gradient descent
Bottou, L · 2010
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. and Lan, G · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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Communication-efficient learning of deep networks from decentralized data
Mcmahan, H. B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. Y · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
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Fully decentralized federated learning
Lalitha, A., Shekhar, S., Javidi, T., and Koushanfar, F · 2018
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Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
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On the convergence of federated optimization in heterogeneous networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
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Peer-to-peer federated learning on graphs
Lalitha, A., Kilinc, O. C., Javidi, T., and Koushanfar, F · 2019
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Braintorrent: A peer-to-peer environment for decentralized federated learning
Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., and Wachinger, C · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Yu, H., Yang, S., and Zhu, S · 2019
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Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
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A unified theory of decentralized sgd with changing topology and local updates
Koloskova, A., Loizou, N., Boreiri, S., Jaggi, M., and Stich, S · 2020
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Learning in the air: Secure federated learning for uav-assisted crowdsensing
Wang, Y., Su, Z., Zhang, N., and Benslimane, A · 2020
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Pyhessian: Neural networks through the lens of the hessian
Yao, Z., Gholami, A., Keutzer, K., and Mahoney, M. W · 2020
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Decentralized accelerated proximal gradient descent
Ye, H., Zhou, Z., Luo, L., and Zhang, T · 2020
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Proactive content caching for internet-of-vehicles based on peer-to-peer federated learning
Yu, Z., Hu, J., Min, G., Xu, H., and Mills, J · 2020
Cited alongside, same era.
Personalized federated learning with first order model optimization
Zhang, M., Sapra, K., Fidler, S., Yeung, S., and Alvarez, J. M · 2020
Cited alongside, same era.
Federated learning based on dynamic regularization
Defed: A principled decentralized and privacy-preserving federated learning algorithm
Yuan, Y., Chen, R., Sun, C., Wang, M., Hua, F., Yi, X., Yang, T., and Liu, J · 2021
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Sharp-maml: Sharpness-aware model-agnostic meta learning
Abbas, M., Xiao, Q., Chen, L., Chen, P., and Chen, T · 2022
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Towards understanding sharpness-aware minimization
Andriushchenko, M. and Flammarion, N · 2022
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Decentralized federated learning: Fundamentals, state-of-the-art, frameworks, trends, and challenges
Beltrán, E. T. M., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., and Celdrán, A. H · 2022
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Improving generalization in federated learning by seeking flat minima
Caldarola, D., Caputo, B., and Ciccone, M · 2022
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Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., and Saligrama, V · 2021
Cited alongside, same era.
Communication efficient primal-dual algorithm for nonconvex nonsmooth distributed optimization
Chen, C., Zhang, J., Shen, L., Zhao, P., and Luo, Z · 2021
Cited alongside, same era.
On bridging generic and personalized federated learning for image classification
Chen, H.-Y. and Chao, W.-L · 2021
Cited alongside, same era.
Efficient sharpness-aware minimization for improved training of neural networks
Du, J., Yan, H., Feng, J., Zhou, J. T., Zhen, L., Goh, R. S. M., and Tan, V · 2021
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2021
Cited alongside, same era.
Personalized cross-silo federated learning on non-iid data
Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., and Zhang, Y · 2021
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2021
Cited alongside, same era.
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Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training
Dai, R., Shen, L., He, F., Tian, X., and Tao, D · 2022
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On the benefits of multiple gossip steps in communication-constrained decentralized federated learning
Hashemi, A., Acharya, A., Das, R., Vikalo, H., Sanghavi, S., and Dhillon, I · 2022
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Blockchain-based federated learning for industrial metaverses: Incentive scheme with optimal aoi
Kang, J., Ye, D., Nie, J., Xiao, J., Deng, X., Wang, S., Xiong, Z., Yu, R., and Niyato, D · 2022
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Towards efficient and scalable sharpness-aware minimization
Liu, Y., Mai, S., Chen, X., Hsieh, C.-J., and You, Y · 2022
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Make sharpness-aware minimization stronger: A sparsified perturbation approach
Mi, P., Shen, L., Ren, T., Zhou, Y., Sun, X., Ji, R., and Tao, D · 2022
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A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
Nguyen, T., Dakka, M., Diakiw, S., VerMilyea, M., Perugini, M., Hall, J., and Perugini, D · 2022
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Generalized federated learning via sharpness aware minimization
Qu, Z., Li, X., Duan, R., Liu, Y., Tang, B., and Lu, Z · 2022
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Decentralized federated averaging
Sun, T., Li, D., and Wang, B · 2022
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Zhang, X., Fang, M., Liu, Z., Yang, H., Liu, J., and Zhu, Z · 2022
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Penalizing gradient norm for efficiently improving generalization in deep learning
Zhao, Y., Zhang, H., and Hu, X · 2022
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Improving sharpness-aware minimization with fisher mask for better generalization on language models
Zhong, Q., Ding, L., Shen, L., Mi, P., Liu, J., Du, B., and Tao, D · 2022
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Topology-aware generalization of decentralized sgd
Zhu, T., He, F., Zhang, L., Niu, Z., Song, M., and Tao, D · 2022
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Make landscape flatter in differentially private federated learning
Shi, Y., Liu, Y., Wei, K., Shen, L., Wang, X., and Tao, D · 2023
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Fedspeed: Larger local interval, less communication round, and higher generalization accuracy
Sun, Y., Shen, L., Huang, T., Ding, L., and Tao, D · 2023
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