Enhancing Energy Management and Efficiency of Asynchronous Federated Split Learning

Distributed machine learning has been developed to address the need for training data processing that has outpaced the increase in server computing power. As processing needs continue to grow, for example, AI-native radio access network (RAN) in 5G/6G networks, it is worth considering ways to promote and improve the efficiency of distributed machine learning. In this study, we focus on improving its energy consumption, while taking advantage of the latest developments in efficiency, especially its combination with split learning.We focus on client selection in the context of asynchronous distributed machine learning, namely asynchronous federated split learning (AFSL), involving heterogeneous clients with non-IID data, some of which may be stragglers or power-limited.To reduce the processing overhead of asynchronous updates while minimizing the impact on accuracy, we propose a clustering and selection scheme. Our approach aims to accelerate convergence and minimize energy consumption by selecting clients that best represent the overall training data in each round.Numerical results are presented on two datasets to highlight the effectiveness of the proposed mechanisms, achieving approximately 23% energy savings on the first dataset and 38% on the second, while meeting the performance targets with fewer training rounds and reduced network overheads.