FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations

NTN-Aware Federated Learning Framework With Hierarchical Satellite Aggregation and LoRA-Based Parameter Efficiency

A unified NTN-aware FL framework that integrates low-rank adaptation (LoRA) with a three-tier hierarchical aggregation architecture that enables a hierarchical aggregation scheme that is otherwise infeasible under LEO visibility constraints is proposed.

Hierarchical Scheduling for Federated Learning Optimization over Deterministic Satellite Networks

H-FedSpace: Hierarchical Federated Learning for Uplink-Constrained LEO Satellite Networks

Low Earth orbit (LEO) satellite federated learning is constrained by short ground-station contacts, intermittent inter-satellite links (ISLs), and stale updates. Under these conditions, the central issue is not whether communication can be reduced in general, but whether uplink traffic to the ground can be reduced without materially weakening learning quality. This paper compares FedSpace and H-FedSpace under one shared implementation, one cached feature benchmark, and one common communication accounting scheme. The results show that H-FedSpace cuts logged uplink traffic by 80.0%, from 133.03 MB to 26.61 MB, while preserving final accuracy at 1.0000. It also achieves a much lower final loss, decreasing from 0.0838 to 0.0014, and records zero logged staleness at evaluation checkpoints. At the same time, these gains are accompanied by 133.03 MB of ISL traffic, a 20.0% increase in total logged communication, and longer runtime. The evidence therefore supports H-FedSpace in the narrower operating regime where satellite-to-ground uplink is the binding resource. The interpretation remains cautious because, in the current implementation, H-FedSpace performs local training on all 12 satellites in each round, whereas FedSpace trains only a randomly selected satellite per round.

Clustered Federated Learning With Contrastive Loss and Staleness-Aware Aggregation for LEO Satellite Constellations

Model Contrastive Federated Learning bridges the gap between distributed learning theory and practical satellite constraints, offering a scalable solution for real-time ML applications in dynamic space-terrestrial networks.

ToCo-Split: Topology-Constrained Communication for Efficient Split Learning in Edge Intelligence

CoCoFL_Continual_Computing_for_Federated_Learning_over_Intermittent_Satellite-Ground_Links

Low earth orbit (LEO) satellite constellations enable geographically distributed ground devices to collaboratively train a global model via federated learning (FL) without sharing raw data, with applications in environmental monitoring and disaster prediction. However, in satellite-assisted FL scenarios, intermittent satellite-ground links allow only a subset of devices to participate in global aggregation within each visibility window, leaving unscheduled devices idle and their local computational and data resources underutilized. Under partial device participation, data heterogeneity among devices may bias the global model toward certain devices, thereby deteriorating learning performance. In this regard, we propose a continual computing based federated learning framework, referred to as CoCoFL, in which scheduled devices participate in the global model aggregation, while unscheduled devices continue updating their local models taking into account model staleness. Guided by the convergence analysis of CoCoFL and subject to visible-window-related time constraints, we jointly optimize the device scheduling and the number of local epochs for scheduled and unscheduled devices. Experimental results demonstrate that CoCoFL achieves faster convergence, lower training loss, and higher test accuracy compared with baselines.