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In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy.
Braintorrent: A peer-to-peer environment for decentralized federated learning
Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019) · 1905
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · 1998
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The MNIST database of handwritten digit images for machine learning research
Deng, L. (2012) · 2012
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2016) · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017) · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · 2017
Earlier work this paper cites.
Electrosense: Open and big spectrum data
Rajendran, S., Calvo-Palomino, R., Fuchs, M., Van den Bergh, B., Cordobes, H., Giustiniano, D., Pollin, S., & Lenders, V. (2018) · 2017
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Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D., Chen, Y., Kannan, R., & Bartlett, P. (2018) · 2018
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Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Xie, C., Koyejo, S., & Gupta, I. (2019) · 2019
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FedML: A research library and benchmark for federated machine learning
He, C., Li, S., So, J., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., Shen, L., Zhao, P., Kang, Y., Liu, Y., Raskar, R., Yang, Q., Annavaram, M., & Avestimehr, S. (2020) · 2020
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Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020) · 2020
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Jointly learning from decentralized (federated) and centralized data to mitigate distribution shift
Hard, A., Partridge, K., Mathews, R., & Augenstein, S. (2021) · 2021
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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., & Dhillon, I. (2022) · 2021
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FATE: An industrial grade platform for collaborative learning with data protection
Liu, Y., Fan, T., Chen, T., Xu, Q., & Yang, Q. (2021) · 2021
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A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets
Sánchez Sánchez, P. M., Jorquera Valero, J. M., Huertas Celdrán, A., Bovet, G., Gil Pérez, M., & Martínez Pérez, G. (2021) · 2021
Cited alongside, same era.
Edge-based communication optimization for distributed federated learning
Wang, T., Liu, Y., Zheng, X., Dai, H.-N., Jia, W., & Xie, M. (2021) · 2021
Cited alongside, same era.
Scatterbrained: A flexible and expandable pattern for decentralized machine learning
Wilt, M., Matelsky, J. K., & Gearhart, A. S. (2021) · 2021
Cited alongside, same era.
Privacy-preserving and syscall-based intrusion detection system for iot spectrum sensors affected by data falsification attacks
Huertas Celdrán, A., Sánchez Sánchez, P. M., Feng, C., Bovet, G., Pérez, G. M., & Stiller, B. (2023b) · 2022
Cited alongside, same era.
Robustness and personalization in federated learning: A unified approach via regularization
Kundu, A., Yu, P., Wynter, L., & Lim, S. H. (2022) · 2022
DFedSN: Decentralized federated learning based on heterogeneous data in social networks
Chen, Y., Liang, L., & Gao, W. (2023) · 2023
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PyTorch Lightning
Falcon, W. (2019) · 2023
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2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial iot
Friha, O., Ferrag, M. A., Benbouzid, M., Berghout, T., Kantarci, B., & Choo, K.-K. R. (2023) · 2023
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Tensorboard
Google (2019a) · 2023
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TensorFlow Federated
Google (2019b) · 2023
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thombashi/tcconfig
Hombashi, T. (2023) · 2023
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CyberSpec: Behavioral fingerprinting for intelligent attacks detection on crowdsensing spectrum sensors
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Cited alongside, same era.
Decentralized federated learning: Balancing communication and computing costs
Liu, W., Chen, L., & Zhang, W. (2022) · 2022
Cited alongside, same era.
Challenges in deploying machine learning: A survey of case studies
Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2022) · 2022
Cited alongside, same era.
Robust aggregation for federated learning
Pillutla, K., Kakade, S. M., & Harchaoui, Z. (2022) · 2022
Cited alongside, same era.
FL-SEC: Privacy-preserving decentralized federated learning using signsgd for the internet of artificially intelligent things
Qu, Y., Xu, C., Gao, L., Xiang, Y., & Yu, S. (2022) · 2022
Cited alongside, same era.
Accelerating decentralized federated learning in heterogeneous edge computing
Wang, L., Xu, Y., Xu, H., Chen, M., & Huang, L. (2022) · 2022
Cited alongside, same era.
Decentralized event-triggered federated learning with heterogeneous communication thresholds
Zehtabi, S., Hosseinalipour, S., & Brinton, C. G. (2022) · 2022
Cited alongside, same era.
P4L: Privacy preserving peer-to-peer learning for infrastructureless setups
Arapakis, I., Papadopoulos, P., Katevas, K., & Perino, D. (2023) · 2023
Cited alongside, same era.
Huertas Celdrán, A., Sánchez Sánchez, P. M., Bovet, G., Martínez Pérez, G., & Stiller, B. (2023a) · 2023
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2023
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DEFEAT: A decentralized federated learning against gradient attacks
Lu, G., Xiong, Z., Li, R., Mohammad, N., Li, Y., & Li, W. (2023) · 2023
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Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
Martínez Beltrán, E. T., Quiles Pérez, M., Sánchez Sánchez, P. M., López Bernal, S., Bovet, G., Gil Pérez, M., Martínez Pérez, G., & Huertas Celdrán, A. (2023b) · 2023
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Model-agnostic federated learning
Mittone, G., Riviera, W., Colonnelli, I., Birke, R., & Aldinucci, M. (2023a) · 2023
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Mender: Open source over-the-air software updates for linux devices
Northern.tech (2022) · 2023
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The digitization of the world from edge to core
Reinsel, D., Gantz, J., & Rydnin, J. (2018) · 2023
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