Improving Federated Learning Performance through Multi-Bit Gradient Quantization and SER-Based Device Selection

Concepts de base

This paper proposes a multi-bit gradient quantization scheme and an inclusive SER-based device selection mechanism to improve the performance of federated learning over wireless networks.

Résumé

The paper analyzes the impact of wireless communication on federated learning (FL) performance through the use of symbol error rate (SER). It proposes a multi-bit quantization scheme for gradient parameters to retain more information and improve transmission error tolerance.
The key highlights are:

Multi-bit quantization of gradient parameters can achieve better FL performance compared to one-bit quantization, but the number of quantization bits needs to be carefully selected based on the specific learning task.

The paper designs an SER-based device selection mechanism (SER-DSM) that allows more users with less than acceptable SER to participate in FL updates, avoiding data waste while achieving better FL performance compared to a mechanism based on packet error rate.

Theoretical analysis shows that the proposed SER-DSM can ensure FL convergence by bounding the upper limit of the expected difference between the global model and the optimal model.

Experiments on MNIST and Fashion-MNIST datasets demonstrate the necessity and superiority of the SER-DSM compared to schemes without device selection or using packet error rate-based selection.

Stats

The paper presents the following key figures and metrics:

Symbol error rate (SER) of each device in gradient transmission, which reflects the influence of communication factors on FL performance (Equation 7).
Upper bound of the expected difference between the global model and the optimal model, which is related to SER and the device selection mechanism (Equation 10).
Condition for guaranteeing FL convergence, which requires the term 4μζ2/(LD)∑K
k=1 Dk·(1-ak·Ξk) to be less than μ/L (Equation 11).

Citations

"The device selection mechanism proposed in this paper allows more users with less than acceptable SER to participate in FL updates, which not only avoids data waste, but also gets better FL performance."
"From the above analysis, it is not difficult to see that the performance of FL increases first and then decreases with the increase of the number of participating users, and reaches the best performance when tr = 1e-2."

Idées clés tirées de

A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning

by Pengcheng Su... à arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02320.pdf

Questions plus approfondies

How can the proposed SER-based device selection mechanism be extended to handle dynamic changes in user communication conditions during the FL training process

To extend the proposed SER-based device selection mechanism to handle dynamic changes in user communication conditions during the FL training process, a dynamic adaptation algorithm can be implemented. This algorithm would continuously monitor the SER of each device and adjust the device selection criteria accordingly. By setting thresholds for acceptable SER levels and dynamically updating them based on real-time feedback, the mechanism can adapt to fluctuations in communication quality. Additionally, incorporating machine learning models that predict future SER based on historical data can help anticipate changes and proactively adjust device selection. This adaptive approach ensures that the FL system remains responsive to varying communication conditions, optimizing performance throughout the training process.

What are the potential trade-offs between the complexity of the SER-based device selection mechanism and the achieved FL performance improvement

The potential trade-offs between the complexity of the SER-based device selection mechanism and the achieved FL performance improvement lie in balancing inclusivity and computational overhead. The complexity of the mechanism, including SER calculations and dynamic device selection, adds computational burden and latency to the system. While the mechanism enhances FL performance by accommodating users with varying communication conditions, the increased complexity can impact real-time decision-making and overall system efficiency. Therefore, optimizing the algorithm for efficiency without compromising accuracy is crucial. Trade-offs may involve fine-tuning parameters, optimizing algorithms for faster execution, and finding the right balance between inclusivity and computational cost to maximize FL performance gains.

How can the multi-bit quantization and SER-based device selection be combined with other techniques, such as model compression or adaptive communication resource allocation, to further enhance the efficiency and robustness of federated learning over wireless networks

Combining multi-bit quantization and SER-based device selection with other techniques like model compression and adaptive communication resource allocation can further enhance the efficiency and robustness of federated learning over wireless networks. Model compression techniques, such as pruning or quantization, can reduce the size of transmitted data, complementing multi-bit quantization to improve communication efficiency. Adaptive resource allocation algorithms can dynamically allocate bandwidth and power based on SER feedback, optimizing communication resources for each user. By integrating these techniques, the FL system can achieve higher accuracy with reduced communication overhead and adapt to changing network conditions effectively. This holistic approach enhances the scalability, reliability, and performance of federated learning over wireless networks.