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Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters.
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Jiang, Y.; Konečný, J.; Rush, K.; and Kannan, S. 2019 · 1909
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On the convergence of local descent methods in federated learning
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The divergence and Bhattacharyya distance measures in signal selection
Kailath, T. 1967 · 1967
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Efficient algorithms for agglomerative hierarchical clustering methods
Day, W. H.; and Edelsbrunner, H. 1984 · 1984
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Backpropagation applied to handwritten zip code recognition
LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; and Jackel, L. D. 1989 · 1989
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A database for handwritten text recognition research
Hull, J. J. 1994 · 1994
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P.; Liu, T.; Ziyin, L.; Salakhutdinov, R.; and Morency, L.-P. 2020a · 2001
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P.; Liu, T.; Ziyin, L.; Salakhutdinov, R.; and Morency, L.-P. 2020b · 2001
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Three Approaches for Personalization with Applications to Federated Learning
Mansour, Y.; Mohri, M.; Ro, J.; and Suresh, A. T. 2020 · 2002
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Adaptive Personalized Federated Learning
Deng, Y.; Kamani, M. M.; and Mahdavi, M. 2020 · 2003
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Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Qian, G.; Sural, S.; Gu, Y.; and Pramanik, S. 2004 · 2004
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Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models
Hershey, J. R.; and Olsen, P. A. 2007 · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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A kernel two-sample test
Gretton, A.; Borgwardt, K. M.; Rasch, M. J.; Schölkopf, B.; and Smola, A. 2012 · 2012
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Low-rank matrix completion using alternating minimization
Jain, P.; Netrapalli, P.; and Sanghavi, S. 2013 · 2013
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Large-scale SVD and manifold learning
Talwalkar, A.; Kumar, S.; Mohri, M.; and Rowley, H. 2013 · 2013
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Network Lasso: Clustering and Optimization in Large Graphs
Hallac, D.; Leskovec, J.; and Boyd, S. P. 2015 · 2015
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Deep Learning Face Attributes in the Wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L.; Healy, J.; and Melville, J. 2018 · 2018
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Federated learning with non-iid data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A.; Mokhtari, A.; and Ozdaglar, A. 2020 · 2020
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An Efficient Framework for Clustered Federated Learning
Ghosh, A.; Chung, J.; Yin, D.; and Ramchandran, K. 2020 · 2020
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S. J.; Stich, S. U.; and Suresh, A. T. 2020 · 2020
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Multi-task Sparse Structure Learning with Gaussian Copula Models
Gonçalves, A. R.; Zuben, F. J. V.; and Banerjee, A. 2016 · 2016
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K. A.; Ivanov, V.; Kreuter, B.; Marcedone, A.; McMahan, H. B.; Patel, S.; Ramage, D.; Segal, A.; and Seth, K. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Federated learning: Collaborative machine learning without centralized training data
McMahan, B.; and Ramage, D. 2017 · 2017
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.
Federated Optimization in Heterogeneous Networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2020 · 2020
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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Wang, J.; Liu, Q.; Liang, H.; Joshi, G.; and Poor, H. V. 2020 · 2020
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Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Mendieta, M.; Yang, T.; Wang, P.; Lee, M.; Ding, Z.; and Chen, C. 2021 · 2021
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Federated Learning with Taskonomy for Non-IID Data
Rad, H. J.; Abdizadeh, M.; and Szabó, A. 2021 · 2021
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Federated Learning from Big Data Over Networks
Sarcheshmehpour, Y.; Leinonen, M.; and Jung, A. 2021 · 2021
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Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints
Sattler, F.; Müller, K.; and Samek, W. 2021 · 2021
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Personalized federated learning by structured and unstructured pruning under data heterogeneity
Vahidian, S.; Morafah, M.; and Lin, B. 2021 · 2021
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Cheung, Y.; Jiang, J.; Yu, F.; and Lou, J. 2022 · 2022
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