Federated Edge Learning (FEEL) emerges as a pioneering distributed machine
learning paradigm for the 6G Hyper-Connectivity, harnessing data from the
Internet of Things (IoT) devices while upholding data privacy. However, current
FEEL algorithms struggle with non-independent and non-identically distributed
(non-IID) data, leading to elevated communication costs and compromised model
accuracy. To address these statistical imbalances within FEEL, we introduce a
clustered data sharing framework, mitigating data heterogeneity by selectively
sharing partial data from cluster heads to trusted associates through
sidelink-aided multicasting. The collective communication pattern is integral
to FEEL training, where both cluster formation and the efficiency of
communication and computation impact training latency and accuracy
simultaneously. To tackle the strictly coupled data sharing and resource
optimization, we decompose the overall optimization problem into the clients
clustering and effective data sharing subproblems. Specifically, a
distribution-based adaptive clustering algorithm (DACA) is devised basing on
three deductive cluster forming conditions, which ensures the maximum sharing
yield. Meanwhile, we design a stochastic optimization based joint computed
frequency and shared data volume optimization (JFVO) algorithm, determining the
optimal resource allocation with an uncertain objective function. The
experiments show that the proposed framework facilitates FEEL on non-IID
datasets with faster convergence rate and higher model accuracy in a limited
communication environment.

基于群集数据共享的联邦边缘学习 (FEEL) 是为 6G 超连接而出现的分布式机器学习范式，通过保护数据隐私，利用物联网设备的数据。为了解决 FEEL 中的统计不平衡问题，我们引入了一个集群数据共享框架，通过辅助多播来选择性地从集群头部共享部分数据给可信合作伙伴，以缓解数据异质性。所提出的框架在有限的通信环境中，能够在非独立和非齐分布的数据集上实现更快的收敛速度和更高的模型准确性。