Leader Selection and Follower Association for UE-centric Distributed Learning in Future Wireless Networks
<|reference_start|>Leader Selection and Follower Association for UE-centric Distributed Learning in Future Wireless Networks: User equipment (UE) devices with high compute performance acting on data with dynamic and stochastic nature to train Artificial Intelligence/Machine Learning (AI/ML) models call for real-time, agile distributed machine learning (DL) algorithms. Consequently, we focus on UE-centric DL algorithms where UEs initiate requests to adapt AI/ML models for better performance, e.g., locally refined AI/ML models among a set of headsets or smartphones. This new setup requires selecting a set of UEs as aggregators (here called leaders) and another set as followers, where all UEs update their models based on their local data, and followers share theirs with leaders for aggregation. From a networking perspective, the first question is how to select leaders and associate followers efficiently. This results in a high dimensional mixed integer programming problem and involves internal UE state information and state information among UEs, called external state information in this paper. To address this challenge, we introduce two new indices: a Leader Internal Index (LII), which is a function of the internal states of each device, demonstrating the willingness to be a leader such as battery life and AI/hardware accelerators, and a Leader eXternal Index (LXI), which is a function of external state information among UEs, such as trust, channel condition, and any aspect relevant for associating a follower with a leader. These two indices transform the highly complex leader selection and follower association problem into a better tractable formulation. More importantly, LIIs and LXIs allow to keep the internal and external state information of this problem inside of each device without compromising users' privacy.<|reference_end|>
@article{parsaeefard2024leader,
    title={Leader Selection and Follower Association for UE-centric Distributed
  Learning in Future Wireless Networks},
    author={Saeedeh Parsaeefard, Sabine Roessel, Anousheh Gholami Ghavamabad,
  Robert Zaus, Bernhard Raaf},
    journal={arXiv preprint arXiv:2409.18268},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18268},
    primaryClass={cs.NI}
}