Fetching the paper…
Reading the bibliography…
The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server.
Gradient-based Learning Applied to Document Recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Small worlds: The dynamics of networks between order and randomness
Watts, D. J · 2000
Earlier work this paper cites.
Gossip-based Computation of Aggregate Information
Kempe, D., Dobra, A., and Gehrke, J · 2003
Earlier work this paper cites.
Chord: a scalable peer-to-peer lookup protocol for internet applications
Stoica, I., Morris, R., Liben-Nowell, D., Karger, D. R., Kaashoek, M. F., Dabek, F., and Balakrishnan, H · 2003
Earlier work this paper cites.
Fast linear iterations for distributed averaging
Xiao, L. and Boyd, S · 2004
Earlier work this paper cites.
Gossip-based aggregation in large dynamic networks
Jelasity, M., Montresor, A., and Babaoglu, Ö · 2005
Earlier work this paper cites.
Gossip-based peer sampling
Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.-M., and Van Steen, M · 2007
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
Earlier work this paper cites.
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
Duchi, J. C., Agarwal, A., and Wainwright, M. J · 2012
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
Earlier work this paper cites.
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Colin, I., Bellet, A., Salmon, J., and Clémençon, S · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Agüera y Arcas, B · 2017
Cited alongside, same era.
Federated Multi-Task Learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
Cited alongside, same era.
Decentralized Collaborative Learning of Personalized Models over Networks
Vanhaesebrouck, P., Bellet, A., and Tommasi, M · 2017
Cited alongside, same era.
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Lian, X., Zhang, W., Zhang, C., and Liu, J · 2018
Cited alongside, same era.
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
Nedić, A., Olshevsky, A., and Rabbat, M. G · 2018
Cited alongside, same era.
D 2 D^{2} : Decentralized Training over Decentralized Data
Tang, H., Lian, X., Yan, M., Zhang, C., and Liu, J · 2018
Tornadoaggregate: Accurate and scalable federated learning via the ring-based architecture
Lee, J.-W., Oh, J., Lim, S., Yun, S.-Y., and Lee, J.-G · 2020
Later among the works it cites.
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
Later among the works it cites.
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Marfoq, O., Xu, C., Neglia, G., and Vidal, R · 2020
Later among the works it cites.
Decentralized gradient methods: does topology matter?
Neglia, G., Xu, C., Towsley, D., and Calbi, G · 2020
Later among the works it cites.
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Zantedeschi, V., Bellet, A., and Tommasi, M · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Personalized Federated Learning with Moreau Envelopes
Dinh, C. T., Tran, N. H., and Nguyen, T. D · 2020
Cited alongside, same era.
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Cited alongside, same era.
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Hanzely, F., Hanzely, S., Horváth, S., and Richtarik, P · 2020
Cited alongside, same era.
The Non-IID Data Quagmire of Decentralized Machine Learning
Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P. B · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2020
Cited alongside, same era.
The MNIST database of handwritten digits
LeCun, Y., Cortes, C., and Burges, C. J · 2020
Cited alongside, same era.
Esfandiari, Y., Tan, S. Y., Jiang, Z., Balu, A., Herron, E., Hegde, C., and Sarkar, S · 2021
Closest in time.
Decentralized learning works: An empirical comparison of gossip learning and federated learning
Hegedüs, I., Danner, G., and Jelasity, M · 2021
Closest in time.
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., D’Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Qi, H., Ramage, D., Raskar, R., Raykova, M., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2021
Closest in time.
Consensus Control for Decentralized Deep Learning
Kong, L., Lin, T., Koloskova, A., Jaggi, M., and Stich, S. U · 2021
Closest in time.
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Lin, T., Karimireddy, S. P., Stich, S. U., and Jaggi, M · 2021
Closest in time.
Federated Multi-Task Learning under a Mixture of Distributions
Marfoq, O., Neglia, G., Bellet, A., Kameni, L., and Vidal, R · 2021
Closest in time.