Khái niệm cốt lõi
Shuffling a small fraction of synthetic data across clients can quadratically reduce the gradient dissimilarity and lead to a super-linear speedup in the convergence of federated learning algorithms under data heterogeneity.
Tóm tắt
The content discusses the impact of data heterogeneity on the convergence rate of federated learning (FL) algorithms, particularly FedAvg. It establishes a precise correspondence between data heterogeneity and the parameters in the convergence rate when a fraction of data is shuffled across clients.
The key highlights are:
- Shuffling can in some cases quadratically reduce the gradient dissimilarity with respect to the shuffling percentage, accelerating convergence.
- Inspired by the theory, the authors propose a practical approach called Fedssyn that addresses the data access rights issue by shuffling locally generated synthetic data.
- Experimental results show that shuffling synthetic data improves the performance of multiple existing FL algorithms by a large margin, even under high data heterogeneity.
- The authors also demonstrate that using Fedssyn can reduce the communication cost by up to 95% compared to vanilla FedAvg.
- Further experiments on differentially private synthetic data generation illustrate the potential of Fedssyn to address privacy concerns in FL.
Overall, the content provides a rigorous theoretical and empirical analysis of the benefits of data shuffling in FL, and proposes a practical framework that leverages synthetic data to address the challenges of data heterogeneity and privacy.
Thống kê
The content does not contain any explicit numerical data or metrics. The key insights are derived from theoretical analysis and empirical evaluations.
Trích dẫn
"Shuffling can in some cases quadratically reduce the gradient dissimilarity with respect to the shuffling percentage, accelerating convergence."
"Inspired by the theory, the authors propose a practical approach called Fedssyn that addresses the data access rights issue by shuffling locally generated synthetic data."
"Experimental results show that shuffling synthetic data improves the performance of multiple existing FL algorithms by a large margin, even under high data heterogeneity."