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

• URL: http://arxiv.org/abs/2604.19399v1
• Date: Tue, 21 Apr 2026 12:26:42 GMT
• Title: Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
• Abstract summary: Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources.<n>This paper focuses on a type of FL where communication between a client and a server is relay-based over dynamic networks.<n>A typical scenario is in-orbit FL, where satellites act as clients and communicate with a server via multi-hop inter-satellite links.<n>This paper presents a comprehensive tractability analysis of routing optimization for in-orbit FL under different settings.
• Score: 8.043709567690376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning [0.39373541926236766]
  Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmission slot.<n>We present AIRPLAN, a query-optimized topology selection framework for Over-the-Air Decentralized Federated Learning (OTA-DFL)<n>We show that AIRPLAN matches the oracle-optimal topology in 91.4% of workloads while introducing less than 1.8% overhead.
  arXiv  Detail & Related papers  (2026-07-05T12:10:47Z)
• Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification [4.674584508653125]
  Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers.<n>We propose an FL approach using few-shot learning and aggregation of the model weights on a global server.<n>An exemplary application of FL is to orchestrate machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers.
  arXiv  Detail & Related papers  (2024-10-21T06:43:04Z)
• Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
  Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources. The lack of model privacy protection in FL becomes an unneglectable challenge. We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
  arXiv  Detail & Related papers  (2023-11-12T11:01:10Z)
• Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning [15.311309249848739]
  Hierarchical independent submodel training (HIST) is a new FL methodology that aims to address these issues in hierarchical cloud-edge-client networks.<n>We demonstrate how HIST can be augmented with over-the-air computation (AirComp) to further enhance the efficiency of the model aggregation over the edge cells.
  arXiv  Detail & Related papers  (2023-10-27T04:42:59Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA [0.0]
  Federated Learning (FL) has emerged as a promising approach for privacy preservation. This article investigates the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss.
  arXiv  Detail & Related papers  (2023-03-29T16:36:42Z)
• Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network [19.23943687834319]
  Federated learning (FL) has emerged as a promising privacy-preserving distributed machine learning framework. We propose a novel FL training framework, dubbed Fed-FSNet, using a properly designed Fuzzy Synthesizing Network (FSNet) to mitigate the Non-I.I.D. at-the-source issue.
  arXiv  Detail & Related papers  (2022-08-21T18:40:51Z)
• Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks [50.68446003616802]
  Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
  arXiv  Detail & Related papers  (2022-02-07T05:11:01Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Federated Learning With Quantized Global Model Updates [84.55126371346452]
  We study federated learning, which enables mobile devices to utilize their local datasets to train a global model. We introduce a lossy FL (LFL) algorithm, in which both the global model and the local model updates are quantized before being transmitted.
  arXiv  Detail & Related papers  (2020-06-18T16:55:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.