FedSynth: Gradient Compression via Synthetic Data in Federated Learning

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D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic · 2017

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G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik · 2017

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Communication-efficient learning of deep networks from decentralized data

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017

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Terngrad: Ternary gradients to reduce communication in distributed deep learning

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signsgd: Compressed optimisation for non-convex problems

J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar · 2018

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Leaf: A benchmark for federated settings,

Original

S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018

Cited alongside, same era.

Dataset distillation

Original

T. Wang, J.-Y. Zhu, A. Torralba, and A. A. Efros · 2018

Cited alongside, same era.

Asynchronous federated optimization

Original

C. Xie, S. Koyejo, and I. Gupta · 2019

Cited alongside, same era.

On biased compression for distributed learning

Original

A. Beznosikov, S. Horváth, P. Richtárik, and M. Safaryan · 2020

Cited alongside, same era.

Federated learning via synthetic data

Original

J. Goetz and A. Tewari · 2020

Later among the works it cites.

Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani · 2020

Later among the works it cites.

Robust and communication-efficient federated learning from non-i.i.d. data

F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek · 2020

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Federated learning with compression: Unified analysis and sharp guarantees

F. Haddadpour, M. M. Kamani, A. Mokhtari, and M. Mahdavi · 2021

Later among the works it cites.