Accelerating Federated Learning Convergence through Synthetic Data Shuffling under Data Heterogeneity

Core Concepts

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.

Abstract

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.

Stats

The content does not contain any explicit numerical data or metrics. The key insights are derived from theoretical analysis and empirical evaluations.

Quotes

"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."

Key Insights Distilled From

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

by Bo L... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2306.13263.pdf

Deeper Inquiries

How can the proposed Fedssyn framework be further extended to handle more complex data modalities beyond images, such as text or audio

The Fedssyn framework can be extended to handle more complex data modalities beyond images, such as text or audio, by adapting the generation and shuffling processes to suit the specific characteristics of these data types. For text data, the framework can involve training language models or text generators on each client using techniques like recurrent neural networks (RNNs) or transformer models. These models can generate synthetic text samples based on the local data distribution. The server can then aggregate, shuffle, and distribute these synthetic text samples to each client for federated learning. Similarly, for audio data, client-specific audio generators can be trained to produce synthetic audio samples. These generators can be based on architectures like WaveNet or other deep learning models suitable for audio generation. The synthetic audio data can be shuffled and distributed among clients for training in a federated learning setting. In both cases, it is essential to ensure that the synthetic data generation process captures the underlying distribution of the data modality accurately. Additionally, privacy and security considerations should be taken into account when handling sensitive data like text or audio in a federated learning setting.

What are the potential drawbacks or limitations of using synthetic data in federated learning, and how can they be addressed

Using synthetic data in federated learning can have potential drawbacks and limitations that need to be addressed to ensure the effectiveness and reliability of the approach: Quality of Synthetic Data: The quality of synthetic data generated by the client models can impact the overall performance of the federated learning process. If the synthetic data does not accurately represent the underlying distribution of the real data, it may lead to biased models and reduced accuracy. Privacy Concerns: Generating and sharing synthetic data may still pose privacy risks, especially if the synthetic data reveals sensitive information about the original data. Differential privacy techniques can be employed to mitigate these privacy concerns and ensure that the shared synthetic data does not leak sensitive information. Computation and Resource Requirements: Training and generating synthetic data on each client may require significant computational resources and time. Efficient algorithms and optimization techniques should be used to minimize the computational overhead and ensure scalability. Generalization to Different Data Modalities: The effectiveness of synthetic data generation may vary across different data modalities. It is essential to validate the approach for diverse types of data to ensure its applicability in various scenarios. To address these limitations, continuous research and development are needed to improve the quality, privacy, efficiency, and generalizability of using synthetic data in federated learning.

How can the insights from this work on the impact of data heterogeneity be applied to other distributed optimization problems beyond federated learning

The insights from this work on the impact of data heterogeneity in federated learning can be applied to other distributed optimization problems beyond federated learning by considering the following: Optimization Algorithms: Similar to federated learning, other distributed optimization problems may face challenges related to data heterogeneity among participating nodes. By understanding how data heterogeneity affects the convergence of optimization algorithms, tailored solutions can be developed to improve performance in distributed settings. Communication Efficiency: The findings on the impact of data shuffling and synthetic data in reducing communication costs and accelerating convergence can be applied to other distributed optimization problems. Strategies like aggregating and shuffling synthetic data or locally generated data can help optimize communication and improve efficiency in distributed settings. Privacy-Preserving Techniques: Insights on privacy concerns and the use of synthetic data in federated learning can be extended to other distributed optimization problems. Techniques like differential privacy and secure aggregation can be employed to protect sensitive information and ensure data privacy in various distributed optimization scenarios. By leveraging the lessons learned from federated learning and adapting them to different distributed optimization problems, researchers can enhance the performance, scalability, and privacy of optimization algorithms in diverse distributed settings.