Federated Learning

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Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
Apr 21, 2026Yi Zhao, Di Yuan, Tao Deng +2Federated Learning Wireless Resource Allocation
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
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Apr 21, 2026Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4Federated Learning Privacy-Preserving Framework
FOCAL-Attention for Heterogeneous Multi-Label Prediction
Apr 21, 2026Chenghao Zhang, Qingqing Long, Ludi Wang +3Multi-Label Classification Text-Attributed Graph
Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
Apr 21, 2026Yuan ShanMental Health Physical Activities
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
Apr 20, 2026H S V N S Kowndinya Renduchintala, Sumit BhatiaLarge Language Model Performance Linguistics
Efficient Federated RLHF via Zeroth-Order Policy Optimization
Apr 20, 2026Deyi Wang, Qining Zhang, Lei YingFederated Reinforcement Learning Reinforcement Learning From Human Feedback
CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks
Apr 20, 2026Yijia Shao, Zora Zhiruo Wang, Neel Ahuja +3Human-Ai Collaboration Collaboration
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Apr 18, 2026Bowen Peng, Yongxiang Liu, Jie Zhou +4Multimodal Pretraining Synthetic Aperture Radar
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Apr 18, 2026Dixi Yao, Tahseen Rabbani, Manzil Zaheer +1Multi-Agent Reasoning Federated Learning
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Apr 17, 2026Aswathi Mundayatt, Jaya Sreevalsan-NairLandslides Temporal Fusion Transformer
Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure
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FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
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First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Mar 6, 2026Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1Stochastic Optimization Federated Learning
Decentralized Federated Learning by Partial Message Exchange
Mar 2, 2026Shan Sha, Shenglong Zhou, Xin Wang +2Federated Learning Differential Privacy
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
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Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
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Feb 16, 2026Qingqian Yang, Hao Wang, Sai Qian Zhang +6Vision-Language Navigation Federated Learning
FedPS: Federated Preprocessing for structured data via aggregated Statistics
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Temperature Scaling Attack Disrupting Model Confidence in Federated Learning
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Dec 31, 2025Yuchao Hou, Zixuan Zhang, Jie Wang +9Synthetic Aperture Radar Federated Learning
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Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning
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End-to-End Facial Expression Detection in Long Videos
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CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
Apr 2, 2025Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5Federated Learning Private Pate-Style Defense
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning Efficient Continual Learning
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Mar 10, 2025Mukesh Sahani, Binanda SenguptaFederated Learning Privacy-Preserving Framework
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Mar 5, 2025Shujie Li, Yuxia Wu, Yuan Fang +1Text-Attributed Graph Graph Neural Networks
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning
Feb 23, 2025Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad SaifFederated Learning
GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
Dec 17, 2024Rui Zhang, Ka-Ho ChowFederated Learning Homomorphic Encryption

Apr 21, 2026 · cs.LGJ/K move · Enter open · S save

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

Yi Zhao, Di Yuan, Tao Deng, Suzhi Cao+1

Department of Information Technology, Uppsala University, 75105 Uppsala, Sweden · School of Computer Science and Technology, Soochow University, Suzhou 215006, China · Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China · School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

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.

Federated Learning Wireless Resource Allocation Satellite Adaptive Routing Distributed Optimization Complex Networks

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