Personalized Decentralized Federated Learning with Knowledge Distillation

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Original

2015

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J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492

Original

2016

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P. Vanhaesebrouck, A. Bellet, and M. Tommasi, “Decentralized collaborative learning of personalized models over networks,” in Artificial Intelligence and Statistics

2017

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B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics

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V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems

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A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi, “Personalized and private peer-to-peer machine learning,” in International Conference on Artificial Intelligence and Statistics

2018

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E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data,” in Workshop on Machine Learning on the Phone and other Consumer Devices (in Conjundtion with NeurIPS 2018)

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R. Anil, G. Pereyra, A. Passos, R. Ormandi, G. E. Dahl, and G. E. Hinton, “Large scale distributed neural network training through online distillation,” in International Conference on Learning Representations

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A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,” arXiv preprint arXiv:1803.02999

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2018

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F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE transactions on neural networks and learning systems

2019

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Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan, “Improving federated learning personalization via model agnostic meta learning,” arXiv preprint arXiv:1909.12488

Original

2019

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J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller, “Inverting gradients - how easy is it to break privacy in federated learning?,” Advances in Neural Information Processing Systems

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S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities, and challenges,” IEEE Communications Magazine

2020

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Y. Huang, L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y. Zhang, “Personalized federated learning: An attentive collaboration approach,” CoRR

Original

2020

Cited alongside, same era.

A. Fallah, A. Mokhtari, and A. Ozdaglar, “Personalized federated learning: A meta-learning approach,” arXiv preprint arXiv:2002.07948

S. Divi, H. Farrukh, and B. Celik, “Unifying distillation with personalization in federated learning,” arXiv preprint arXiv:2105.15191

Original

2021

Later among the works it cites.

E. Borodich, A. Beznosikov, A. Sadiev, V. Sushko, N. Savelyev, M. Takáč, and A. Gasnik, “Decentralized personalized federated min-max problems,” in Workshop on New Frontiers in Federated Learning (in Conjunction with NeurIPS 2021)

2021

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O. Marfoq, G. Neglia, A. Bellet, L. Kameni, and R. Vidal, “Federated multi-task learning under a mixture of distributions,” Advances in Neural Information Processing Systems

2021

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O. Marfoq, G. Neglia, R. Vidal, and L. Kameni, “Personalized federated learning through local memorization,” in International Conference on Machine Learning

2022

Later among the works it cites.