Authors: Mengchen Fan, Keren Li, Tianyun Zhang, Qing Tian, Baocheng Geng
Abstract: Distributed Learning (DL) enables the training of machine learning models
across multiple devices, yet it faces challenges like non-IID data
distributions and device capability disparities, which can impede training
efficiency. Communication bottlenecks further complicate traditional Federated
Learning (FL) setups. To mitigate these issues, we introduce the Personalized
Federated Learning with Decentralized Selection Training (PFedDST) framework.
PFedDST enhances model training by allowing devices to strategically evaluate
and select peers based on a comprehensive communication score. This score
integrates loss, task similarity, and selection frequency, ensuring optimal
peer connections. This selection strategy is tailored to increase local
personalization and promote beneficial peer collaborations to strengthen the
stability and efficiency of the training process. Our experiments demonstrate
that PFedDST not only enhances model accuracy but also accelerates convergence.
This approach outperforms state-of-the-art methods in handling data
heterogeneity, delivering both faster and more effective training in diverse
and decentralized systems.
Source: http://arxiv.org/abs/2502.07750v1
