On reducing the communication cost of the diffusion LMS algorithm
This work addresses energy efficiency in distributed learning for ad-hoc networks, though it is incremental as it builds upon existing diffusion LMS methods.
The paper tackles the challenge of high communication costs in diffusion LMS algorithms for ad-hoc networks with limited energy budgets, introducing a strategy that reduces these costs while maintaining performance, as confirmed by theoretical analysis and numerical experiments.
The rise of digital and mobile communications has recently made the world more connected and networked, resulting in an unprecedented volume of data flowing between sources, data centers, or processes. While these data may be processed in a centralized manner, it is often more suitable to consider distributed strategies such as diffusion as they are scalable and can handle large amounts of data by distributing tasks over networked agents. Although it is relatively simple to implement diffusion strategies over a cluster, it appears to be challenging to deploy them in an ad-hoc network with limited energy budget for communication. In this paper, we introduce a diffusion LMS strategy that significantly reduces communication costs without compromising the performance. Then, we analyze the proposed algorithm in the mean and mean-square sense. Next, we conduct numerical experiments to confirm the theoretical findings. Finally, we perform large scale simulations to test the algorithm efficiency in a scenario where energy is limited.