FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated learning. However, due to the staleness introduced by the asynchronous process, its performance may degrade in some scenarios. Existing methods often use the round difference between the current model and the global model as the sole measure of staleness, which is coarse-grained and lacks observation of the model itself, thereby limiting the performance ceiling of asynchronous methods. In this paper, we propose FedPSA (Parameter Sensitivity-based Asynchronous Federated Learning), a more fine-grained AFL framework that leverages parameter sensitivity to measure model obsolescence and establishes a dynamic momentum queue to assess the current training phase in real time, thereby adjusting the to
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated learning. However, due to the staleness introduced by the asynchronous process, its performance may degrade in some scenarios. Existing methods often use the round difference between the current model and the global model as the sole measure of staleness, which is coarse-grained and lacks observation of the model itself, thereby limiting the performance ceiling of asynchronous methods. In this paper, we propose FedPSA (Parameter Sensitivity-based Asynchronous Federated Learning), a more fine-grained AFL framework that leverages parameter sensitivity to measure model obsolescence and establishes a dynamic momentum queue to assess the current training phase in real time, thereby adjusting the tolerance for outdated information dynamically. Extensive experiments on multiple datasets and comparisons with various methods demonstrate the superior performance of FedPSA, achieving up to 6.37\% improvement over baseline methods and 1.93\% over the current state-of-the-art method.
Executive Summary
This paper proposes FedPSA, a novel asynchronous federated learning (AFL) framework that addresses the issue of behavioral staleness in AFL. FedPSA leverages parameter sensitivity to measure model obsolescence and adjusts the tolerance for outdated information dynamically using a dynamic momentum queue. The framework demonstrates superior performance in experiments on multiple datasets, achieving up to 6.37% improvement over baseline methods. While FedPSA presents a promising solution to the staleness problem in AFL, its applicability and scalability in real-world scenarios warrant further investigation.
Key Points
- ▸ FedPSA is a novel AFL framework that addresses behavioral staleness
- ▸ FedPSA uses parameter sensitivity to measure model obsolescence
- ▸ Dynamic momentum queue adjusts tolerance for outdated information
Merits
Strength in addressing staleness
FedPSA effectively tackles the staleness issue in AFL, enabling faster training speed and improved performance.
Improved performance
FedPSA demonstrates up to 6.37% improvement over baseline methods and 1.93% over the current state-of-the-art method.
Demerits
Limited scalability
The proposed framework may face challenges in scaling to large, complex datasets or real-world applications.
Dependence on parameter sensitivity
The framework's performance is contingent on the accuracy of parameter sensitivity measurements, which may be affected by various factors.
Expert Commentary
FedPSA represents a significant step forward in addressing the staleness issue in AFL, a critical challenge that has hindered the widespread adoption of federated learning. The proposed framework's use of parameter sensitivity and dynamic momentum queue is a novel approach that warrants further investigation. While the framework demonstrates impressive performance improvements, its scalability and applicability in real-world scenarios require careful consideration. Ultimately, FedPSA has the potential to transform the field of federated learning, enabling the development of more efficient, secure, and reliable distributed machine learning systems.
Recommendations
- ✓ Further research is needed to explore the scalability and applicability of FedPSA in real-world scenarios.
- ✓ Investigating the use of FedPSA in conjunction with other AFL frameworks could lead to even more significant performance improvements.
