ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
arXiv:2603.04437v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource alloc
arXiv:2603.04437v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource allocation decisions. We propose an online optimization enhanced block coordinate descent (OOE-BCD) algorithm to solve the problem iteratively. Experimental results show that when compared with five baseline schemes, our proposed ASFL framework converges faster and reduces the total delay and energy consumption by up to 75% and 80%, respectively.
Executive Summary
The proposed Adaptive Split Federated Learning (ASFL) framework exhibits significant improvements in convergence rate, delay, and energy consumption compared to existing methods. By leveraging the central server's computation resources and employing adaptive model splitting and resource allocation, ASFL optimizes learning performance and efficiency. Experimental results demonstrate ASFL's superiority, achieving up to 75% reduction in total delay and 80% reduction in energy consumption. However, the framework's applicability to heterogeneous networks and scalability remain areas for further investigation. Furthermore, the impact of non-ideal network conditions, such as packet loss and latency, on ASFL's performance warrants exploration. The findings of this study have significant implications for the widespread adoption of federated learning in resource-constrained environments.
Key Points
- ▸ Adaptive model splitting enables efficient resource allocation
- ▸ Central server's computation resources are leveraged to improve convergence rate
- ▸ Optimization problem solved through online optimization enhanced block coordinate descent algorithm
Merits
Strength in Optimizing Learning Performance
ASFL's adaptive model splitting and resource allocation effectively optimize learning performance, enabling faster convergence rates.
Demerits
Limitation in Addressing Heterogeneous Networks
The framework's applicability to heterogeneous networks and scalability require further investigation to ensure widespread adoption.
Expert Commentary
While the proposed ASFL framework demonstrates significant improvements in convergence rate, delay, and energy consumption, its practicality and scalability in real-world applications require further evaluation. Moreover, the impact of non-ideal network conditions on ASFL's performance necessitates careful consideration. Nevertheless, the findings of this study contribute significantly to the development of more efficient federated learning frameworks, with the potential to revolutionize machine learning in resource-constrained environments.
Recommendations
- ✓ Future research should focus on addressing the limitations of ASFL in heterogeneous networks and scalability.
- ✓ Investigation into the framework's performance under non-ideal network conditions is necessary.