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Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregation.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H. B., et al · 1902
Earlier work this paper cites.
New directions in cryptography
Diffie, W. and Hellman, M · 1976
Earlier work this paper cites.
How to share a secret
Shamir, A · 1979
Earlier work this paper cites.
Protocols for secure computations
Yao, A. C · 1982
Earlier work this paper cites.
On mds codes via cauchy matrices
Roth, R. M. and Lempel, A · 1989
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Fednas: Federated deep learning via neural architecture search
He, C., Annavaram, M., and Avestimehr, S · 2004
Earlier work this paper cites.
Secure byzantine-robust machine learning
He, L., Karimireddy, S. P., and Jaggi, M · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Earlier work this paper cites.
A cloud benchmark suite combining micro and applications benchmarks
Scheuner, J. and Leitner, P · 2018
Earlier work this paper cites.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, B., Van Overveldt, T., Petrou, D., Ramage, D., and Roselander, J · 2019
Earlier work this paper cites.
Federated learning with autotuned communication-efficient secure aggregation
Bonawitz, K., Salehi, F., Konečnỳ, J., McMahan, B., and Gruteser, M · 2019
Earlier work this paper cites.
Searching for mobilenetv3
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al · 2019
Earlier work this paper cites.
On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Earlier work this paper cites.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., and Qi, H · 2019
Cited alongside, same era.
Asynchronous federated optimization
Xie, C., Koyejo, S., and Gupta, I · 2019
Cited alongside, same era.
Lagrange coded computing: Optimal design for resiliency, security, and privacy
Yu, Q., Li, S., Raviv, N., Kalan, S. M. M., Soltanolkotabi, M., and Avestimehr, S. A · 2019
Cited alongside, same era.
Fedopt: Towards communication efficiency and privacy preservation in federated learning
Asad, M., Moustafa, A., and Ito, T · 2020
Cited alongside, same era.
Secure single-server aggregation with (poly) logarithmic overhead
Bell, J. H., Bonawitz, K. A., Gascón, A., Lepoint, T., and Raykova, M · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V · 2020
Later among the works it cites.
Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval
Weyand, T., Araujo, A., Cao, B., and Sim, J · 2020
Later among the works it cites.
Deep leakage from gradients
Zhu, L. and Han, S · 2020
Later among the works it cites.
https://grpc.io/ , 2021
gRPC: A high performance, open source universal RPC framework · 2021
Closest in time.
https://pytorch.org/docs/stable/rpc.html , 2021
Pytorch rpc: Distributed deep learning built on tensor-optimized remote procedure calls · 2021
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Basil: A fast and byzantine-resilient approach for decentralized training
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Chai, Z., Chen, Y., Zhao, L., Cheng, Y., and Rangwala, H · 2020
Cited alongside, same era.
Asynchronous online federated learning for edge devices with non-iid data
Chen, Y., Ning, Y., Slawski, M., and Rangwala, H · 2020
Cited alongside, same era.
Secure aggregation with heterogeneous quantization in federated learning
Elkordy, A. R. and Avestimehr, A. S · 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.
Inverting gradients - how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 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.
Central server free federated learning over single-sided trust social networks
He, C., Tan, C., Tang, H., Qiu, S., and Liu, J · 2020
Cited alongside, same era.
Elkordy, A. R., Prakash, S., and Avestimehr, A. S · 2021
Closest in time.
Fairfed: Enabling group fairness in federated learning
Ezzeldin, Y. H., Yan, S., He, C., Ferrara, E., and Avestimehr, S · 2021
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Fedgraphnn: A federated learning system and benchmark for graph neural networks
He, C., Balasubramanian, K., Ceyani, E., Yang, C., Xie, H., Sun, L., He, L., Yang, L., Yu, P. S., Rong, Y., et al · 2021
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Spreadgnn: Serverless multi-task federated learning for graph neural networks
He, C., Ceyani, E., Balasubramanian, K., Annavaram, M., and Avestimehr, S · 2021
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Learning from history for byzantine robust optimization
Karimireddy, S. P., He, L., and Jaggi, M · 2021
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Omnilytics: A blockchain-based secure data market for decentralized machine learning
Liang, J., Li, S., Jiang, W., Cao, B., and He, C · 2021
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Fednlp: A research platform for federated learning in natural language processing
Lin, B. Y., He, C., Zeng, Z., Wang, H., Huang, Y., Soltanolkotabi, M., Ren, X., and Avestimehr, S · 2021
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Advances and open problems in federated learning
McMahan, H. B. et al · 2021
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Throughput prediction using machine learning in lte and 5g networks
Minovski, D., Ogren, N., Ahlund, C., and Mitra, K · 2021
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Spider: Searching personalized neural architecture for federated learning
Mushtaq, E., He, C., Ding, J., and Avestimehr, S · 2021
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Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Esmaeili, M. M., and Huba, D · 2021
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Verifiable coded computing: Towards fast, secure and private distributed machine learning
Tang, T., Ali, R. E., Hashemi, H., Gangwani, T., Avestimehr, S., and Annavaram, M · 2021
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A field guide to federated optimization
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., Data, D., et al · 2021
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Information theoretic secure aggregation with user dropouts
Zhao, Y. and Sun, H · 2021
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