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Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data.
Inequalities
Hardy, G. H., Littlewood, J. E., Pólya, G., Pólya, G., et al · 1952
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Hierarchical grouping to optimize an objective function
Ward Jr, J. H · 1963
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A model of inductive bias learning
Baxter, J · 2000
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Personalized federated learning with moreau envelopes
Dinh, C. T., Tran, N. H., and Nguyen, T. D · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Structured sparsity and generalization
Maurer, A. and Pontil, M · 2012
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Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 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
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Federated Multi-Task Learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A · 2017
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Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
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Towards taming the resource and data heterogeneity in federated learning
Chai, Z., Fayyaz, H., Fayyaz, Z., Anwar, A., Zhou, Y., Baracaldo, N., Ludwig, H., and Cheng, Y · 2019
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Variational federated multi-task learning, 2019
Corinzia, L. and Buhmann, J. M · 2019
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Improving Federated Learning Personalization via Model Agnostic Meta Learning
Jiang, Y., Konečný, J., Rush, K., and Kannan, S · 2019
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Adaptive gradient-based meta-learning methods
Khodak, M., Balcan, M.-F. F., and Talwalkar, A. S · 2019
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Fedmd: Heterogenous federated learning via model distillation
Li, D. and Wang, J · 2019
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On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2019
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Differentiable learning-to-normalize via switchable normalization
Luo, P., Ren, J., Peng, Z., Zhang, R., and Li, J · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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Deep leakage from gradients
Zhu, L. and Han, S · 2019
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Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Briggs, C., Fan, Z., and Andras, P · 2020
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Tifl: A tier-based federated learning system
Chai, Z., Ali, A., Zawad, S., Truex, S., Anwar, A., Baracaldo, N., Zhou, Y., Ludwig, H., Yan, F., and Cheng, Y · 2020
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Model compression and hardware acceleration for neural networks: A comprehensive survey
Deng, L., Li, G., Han, S., Shi, L., and Xie, Y · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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An efficient framework for clustered federated learning
Ghosh, A., Chung, J., Yin, D., and Ramchandran, K · 2020
Cited alongside, same era.
Fedboost: A communication-efficient algorithm for federated learning
Hamer, J., Mohri, M., and Suresh, A. T · 2020
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
He, C., Annavaram, M., and Avestimehr, S · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Cross-node federated graph neural network for spatio-temporal data modeling
Meng, C., Rambhatla, S., and Liu, Y · 2021
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Neurips 2021, workshop on new frontiers in federated learning
Minh, H. and Carl, K · 2021
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Quped: Quantized personalization via distillation with applications to federated learning
Ozkara, K., Singh, N., Data, D., and Diggavi, S · 2021
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Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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Personalized federated learning using hypernetworks
Shamsian, A., Navon, A., Fetaya, E., and Chechik, G · 2021
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Federated reconstruction: Partially local federated learning
Singhal, K., Sidahmed, H., Garrett, Z., Wu, S., Rush, J., and Prakash, S · 2021
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Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
Cited alongside, same era.
Think locally, act globally: Federated learning with local and global representations
Liang, P. P., Liu, T., Ziyin, L., Salakhutdinov, R., and Morency, L.-P · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
Cited alongside, same era.
Fedfast: Going beyond average for faster training of federated recommender systems
Muhammad, K., Wang, Q., O’Reilly-Morgan, D., Tragos, E., Smyth, B., Hurley, N., Geraci, J., and Lawlor, A · 2020
Cited alongside, same era.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R · 2020
Cited alongside, same era.
Fetchsgd: Communication-efficient federated learning with sketching
Rothchild, D., Panda, A., Ullah, E., Ivkin, N., Stoica, I., Braverman, V., Gonzalez, J., and Arora, R · 2020
Cited alongside, same era.
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Towards personalized federated learning
Tan, A. Z., Yu, H., Cui, L., and Yang, Q · 2021
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Data-free knowledge distillation for heterogeneous federated learning
Zhu, Z., Hong, J., and Zhou, J · 2021
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Git re-basin: Merging models modulo permutation symmetries
Ainsworth, S. K., Hayase, J., and Srinivasa, S · 2022
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Federated dynamic sparse training: Computing less, communicating less, yet learning better
Bibikar, S., Vikalo, H., Wang, Z., and Chen, X · 2022
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pFL-bench: A comprehensive benchmark for personalized federated learning
Chen, D., Gao, D., Kuang, W., Li, Y., and Ding, B · 2022
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Efficient split-mix federated learning for on-demand and in-situ customization
Hong, J., Wang, H., Wang, Z., and Zhou, J · 2022
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Achieving personalized federated learning with sparse local models
Huang, T., Liu, S., Li, S., He, F., Lin, W., and Tao, D · 2022
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Model pruning enables efficient federated learning on edge devices
Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W.-H., Leung, K. K., and Tassiulas, L · 2022
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On the convergence of clustered federated learning
Ma, J., Long, G., Zhou, T., Jiang, J., and Zhang, C · 2022
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Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., and Huba, D · 2022
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ZeroFL: Efficient on-device training for federated learning with local sparsity
Qiu, X., Fernandez-Marques, J., Gusmao, P. P., Gao, Y., Parcollet, T., and Lane, N. D · 2022
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Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning
Wang, Z., Kuang, W., Xie, Y., Yao, L., Li, Y., Ding, B., and Zhou, J · 2022
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What do we mean by generalization in federated learning?
Yuan, H., Morningstar, W. R., Ning, L., and Singhal, K · 2022
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FS-REAL: Towards real-world cross-device federated learning
Chen, D., Gao, D., Xie, Y., Pan, X., Li, Z., Li, Y., Ding, B., and Zhou, J · 2023
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Revisiting personalized federated learning: Robustness against backdoor attacks
Qin, Z., Yao, L., Chen, D., Li, Y., Ding, B., and Cheng, M · 2023
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Federatedscope: A flexible federated learning platform for heterogeneity
Xie, Y., Wang, Z., Gao, D., Chen, D., Yao, L., Kuang, W., Li, Y., Ding, B., and Zhou, J · 2023
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