… data heterogeneity, and cooperative learning, which is the … system model for federated
learning where edge servers are … , we propose a semi-decentralized architecture, where D2D …

… Abstract—We present a semi-decentralized federated learning … accuracy in comparison
with the federated averaging algorithm. … INTRODUCTION Federated learning (FL) algorithms …

… In this section, we first introduce the semi-decentralized FEEL system, and a training …
Semi-Decentralized FEEL System We consider an edge-assisted federated learning system as …

… sharing, SD-FEEL incorporates more training data, while enjoying lower latency compared
with conventional federated learning. We detail the training algorithm for SD-FEEL with three …

… is a major bottleneck in federated edge learning frameworks. The … We propose a semi-decentralized
federated learning … outperforms federated averaging-based benchmarks for learning …

… To overcome these challenges, this paper presents Fedstellar, a novel platform designed
to train FL models in a decentralized, semi-decentralized, and centralized fashion across …

… Specifically, in our semi-decentralized methodology, D2D consensus updates are injected
into the federated averaging framework based on column-stochastic weight matrices that …

… developing learning models while maintaining data privacy. In this paper, we concentrate
on a semi-decentralized FEEL (SD-FEEL) framework, taking into account the limited training …

… is assigned a specific task within the federated learning process. Furthermore, the … semi-decentralized,
and centralized architecture. The semidecentralized approach can be seen as a …

… is a major bottleneck in federated edge learning frameworks. The … We propose a semi-decentralized
federated learning … outperforms federated averaging-based benchmarks for learning …