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Federated Learning via Synthetic Data

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arxiv 2008.04489 v2 pith:XVQJ7FFE submitted 2020-08-11 cs.LG stat.ML

Jack Goetz , Ambuj Tewari This is my paper · ORCID
classification cs.LGstat.ML
keywords dataclientsfederatedlearningmodelcostsinsteadmethod
verification ladder T0 review T1 audit T2 compute T3 formal
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Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit model parameters (or updates), which for modern neural networks can be on the scale of millions of parameters, inflicting significant computational costs on the clients. We propose a method for federated learning where instead of transmitting a gradient update back to the server, we instead transmit a small amount of synthetic `data'. We describe the procedure and show some experimental results suggesting this procedure has potential, providing more than an order of magnitude reduction in communication costs with minimal model degradation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dataset Distillation Based on Saliency-Driven Prototype Alignment

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Saliency-guided latent prototypes plus confidence-based hard-prototype refinement improve diffusion-based dataset distillation accuracy on ImageNet subsets, CIFAR, and ImageNet-1K without fine-tuning the generative backbone.

  2. FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios

    cs.LG 2025-07 reject novelty 6.0 of 10

    FedWCM uses per-client data-distribution scores to adapt momentum and aggregation weights in federated learning, showing empirical gains over FedAvg and FedCM on long-tailed non-IID datasets, but its convergence proof...

  3. E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing

    cs.LG 2025-02 conditional novelty 4.0 of 10

    A federated-learning gradient compressor that sends tiny synthetic features instead of gradients, plus a download-phase compressor and a budget scheduler, with conditional convergence proofs and broad experiments.

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