Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

2026-04-21

AI Summary

This study addresses the optimal routing problem in dynamic satellite networks for spaceborne federated learning, systematically analyzing the computational complexity of global model dissemination and local model collection under multidimensional communication settings—including unicast versus multicast, splittable versus non-splittable flows, and client selection strategies. Leveraging computational complexity theory, combinatorial optimization, and network flow techniques, the work provides the first complete characterization of the tractability boundary for this routing problem: it rigorously establishes which scenarios are solvable in polynomial time and which are NP-hard, and develops efficient algorithms for all tractable cases. These results fill a critical theoretical gap in routing optimization for spaceborne federated learning and lay a solid foundation for its practical system design and deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where communication between a client and the server is relay-based over dynamic networks, making routing optimization essential. A typical scenario is in-orbit FL, where satellites act as clients and communicate with a server (which can be a satellite, ground station, or aerial platform) via multi-hop inter-satellite links. This paper presents a comprehensive tractability analysis of routing optimization for in-orbit FL under different settings. For global model distribution, these include the number of models, the objective function, and routing schemes (unicast versus multicast, and splittable versus unsplittable flow). For local model collection, the settings consider the number of models, client selection, and flow splittability. For each case, we rigorously prove whether the global optimum is obtainable in polynomial time or the problem is NP-hard. Together, our analysis draws clear boundaries between tractable and intractable regimes for a broad spectrum of routing problems for in-orbit FL. For tractable cases, the derived efficient algorithms are directly applicable in practice. For intractable cases, we provide fundamental insights into their inherent complexity. These contributions fill a critical yet unexplored research gap, laying a foundation for principled routing design, evaluation, and deployment in satellite-based FL or similar distributed learning systems.

Problem

• Federated Learning
• Satellite Networks
• Routing Optimization
• In-orbit FL
• Tractability

Innovation

• Federated Learning
• Satellite Networks
• Routing Optimization
• Tractability Analysis
• NP-hardness