Decentralized Personalized Federated Learning with Conditional Sparse-to-Sparser Training

Conceptos Básicos

The proposed DA-DPFL framework effectively balances communication, training efficiency, and data heterogeneity in decentralized personalized federated learning by incorporating dynamic aggregation and sparse-to-sparser training.

Resumen

The content discusses a novel Decentralized Personalized Federated Learning (DA-DPFL) framework that addresses the challenges of communication cost, training efficiency, and data heterogeneity in federated learning.
Key highlights:

DA-DPFL introduces a dynamic aggregation scheduling policy that allows clients to reuse previously trained models within the same communication round, accelerating convergence.
It incorporates a dynamic pruning strategy that progressively reduces the number of model parameters during training, leading to substantial energy savings while retaining adequate information.
Comprehensive experiments show that DA-DPFL outperforms centralized and decentralized federated learning baselines in test accuracy, while achieving up to 5 times reduction in energy costs.
Theoretical analysis is provided to solidify the convergence properties of DA-DPFL in decentralized and personalized learning settings.

Estadísticas

The number of communication rounds required to attain a specified error level ε is lower for DA-DPFL compared to the DisPFL model.
The ratio of the error bound for DA-DPFL and DisPFL is 3M+2/2M+2, which approaches 3/2 as M increases.

Citas

"DA-DPFL not only addresses data heterogeneity efficiently via masked-based PFL but also significantly improves convergence speed by incorporating a fair dynamic communication protocol."
"DA-DPFL achieves comparative or even superior model performance across various tasks and DNN architectures compared to both CFL and DFL baselines."
"The proposed learning method with dynamic aggregation achieves the highest energy and communication efficiency."

Ideas clave extraídas de

Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme

by Qianyu Long,... a las arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15943.pdf

Consultas más profundas

How can the dynamic aggregation and sparse-to-sparser training strategies in DA-DPFL be extended to other decentralized learning paradigms beyond federated learning

The dynamic aggregation and sparse-to-sparser training strategies in DA-DPFL can be extended to other decentralized learning paradigms by adapting the principles to different contexts. For example, in collaborative edge computing environments, where multiple edge devices collaborate to perform machine learning tasks, the dynamic aggregation approach can be utilized to optimize model aggregation and training. By incorporating sparse-to-sparser training techniques, edge devices can efficiently communicate and update models while minimizing energy consumption and latency. Additionally, in distributed sensor networks for IoT applications, the sparse-to-sparser training strategy can be applied to reduce communication overhead and improve model convergence across heterogeneous sensor data.

What are the potential drawbacks or limitations of the DA-DPFL approach, and how can they be addressed in future work

One potential drawback of the DA-DPFL approach is the trade-off between model sparsity and performance. While the dynamic pruning and aggregation strategies in DA-DPFL aim to reduce communication and training costs, there may be instances where aggressive pruning leads to a loss of important information and impacts model accuracy. To address this limitation, future work could focus on developing adaptive pruning algorithms that dynamically adjust the pruning intensity based on the model's performance during training. Additionally, exploring ensemble learning techniques to combine multiple sparse models generated during training could help mitigate the impact of aggressive pruning on model performance.

Given the theoretical and empirical advantages of DA-DPFL, how might it impact the broader landscape of distributed and decentralized machine learning systems

The theoretical and empirical advantages of DA-DPFL have the potential to significantly impact the broader landscape of distributed and decentralized machine learning systems. By demonstrating superior performance in terms of convergence speed, model accuracy, and energy efficiency, DA-DPFL sets a new standard for decentralized learning frameworks. This approach could pave the way for more efficient and scalable machine learning systems in various domains, including healthcare, finance, and smart cities. The optimization techniques and scheduling policies employed in DA-DPFL could inspire the development of new algorithms and methodologies for decentralized learning, leading to advancements in edge computing, IoT, and federated learning research.