FedSynth: Gradient Compression via Synthetic Data in Federated Learning
This addresses communication bottlenecks in federated learning for distributed systems, but it is incremental as it builds on prior compression methods.
The paper tackles the problem of high communication costs in federated learning by proposing a method where clients transmit synthetic data instead of model updates, achieving comparable or better performance than random masking baselines on three benchmark datasets.
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that could desparately affect the global model accuracy. In this work, we propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic dataset such that using it as the training data, the model performs similarly well on the real training data. The server will recover the local model update via the synthetic data and apply standard aggregation. We then provide a new algorithm FedSynth to learn the synthetic data locally. Empirically, we find our method is comparable/better than random masking baselines in all three common federated learning benchmark datasets.