Federated Learning

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619 papers

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On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
May 12, 2026Kexin Zhang, Lixin Li, Yuna Yan +5Unmanned Aerial Vehicles Semantic Communications
Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
May 12, 2026Satwat Bashir, Tasos Dagiuklas, Muddesar IqbalHeterogeneous Federated Learning Federated Learning
Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
May 12, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Federated Learning Partially Observable Markov Decision Process
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
May 12, 2026Alberto Fernández-Hernández, Jose I. Mestre, Cristian Pérez-Corral +3Federated Learning Fedavg
The Scaling Law of Evaluation Failure: Why Simple Averaging Collapses Under Data Sparsity and Item Difficulty Gaps, and How Item Response Theory Recovers Ground Truth Across Domains
May 11, 2026Jung Min KangArtificial Intelligence Benchmarks Item Response Theory
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
May 11, 2026Ben Rachmut, Luise Ge, William Yeoh +2Heterogeneous Federated Learning Federated Learning
FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
May 11, 2026Fatima Z. Abacha, Sin G. Teo, Yuanxiang Wu +2Federated Learning Backdoor Attacks
SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels
May 11, 2026Zahra Hafezi Kafshgari, Hadi Hadizadeh, Parvaneh SaeediMedical Image Segmentation Federated Learning
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
May 11, 2026Li Shen, Xiaolei Hao, Qinglun Li +3Federated Learning Vision Transformer
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
May 11, 2026Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2Federated Learning Differential Privacy
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
May 11, 2026Zhenhao Shen, Zeming Yang, Yue Chen +5Generalizable Robotic Manipulations Manipulation
Concordia: Self-Improving Synthetic Tables for Federated LLMs
May 11, 2026Jimin Huang, Duanyu Feng, Nuo Chen +8Federated Learning Large Language Model Adaptation
Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
May 10, 2026Su Wang, Mung Chiang, H. Vincent PoorFederated Learning Heterogeneous Edge Devices
M $^2$ FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
May 10, 2026Manjil Nepal, Kimsie Phan, Tamoghna Ojha +2Heterogeneous Edge Devices Federated Learning
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
May 10, 2026Antonios Makris, Christos Dousis, Emmanouil Kritharakis +2Federated Learning Heterogeneity
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
May 10, 2026Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3Generalist Graph Anomaly Detection Federated Graph Learning
Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
May 10, 2026Akihito Taya, Yuuki Nishiyama, Kaoru SezakiFederated Learning Distributional Learning
The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support
May 9, 2026Kyriakos ChristodoulidesPayment Networks First-Price Auctions
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
May 9, 2026Bingnan Xiao, Yuan Gao, Bingcong Li +3Federated Learning Sharpness-Aware Minimization
Evaluating Federated Learning approaches for mammography under breast density heterogeneity
May 9, 2026Gonzalo Iñaki Quintana, Franco Martin Di Maria, Laurence VancambergFederated Learning Mammography
MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift
May 9, 2026Kiran Naseer, Naveed Anwer ButtBrain Tumor Segmentation Federated Learning
When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
May 9, 2026Kiran Naseer, Umar ShoaibFederated Learning Fairness-Aware Learning
FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference
May 9, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Federated Learning Fedavg
Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
May 8, 2026Guangchen LanFederated Reinforcement Learning Deep Reinforcement Learning
Private Vertical Federated Inference for Time-Series
May 8, 2026Lucas Fenaux, Larris Xie, Aditya Bang +3Federated Learning Homomorphic Encryption
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback
May 8, 2026Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2Federated Learning Self-Play
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
May 8, 2026Fabian Stricker, Jose A. Peregrina, David Bermbach +1Federated Learning Model Evaluation
Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
May 8, 2026Hanlin Cai, Kai Li, Houtianfu Wang +4Large Language Model Fine-Tuning Language Model Evasion Attacks
Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
May 8, 2026Ozgu Goksu, Nicolas PugeaultFederated Learning Distributional Learning
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
May 8, 2026Usevalad Milasheuski, Piero Baraldi, Enrico Zio +1Federated Learning Predictive Maintenance
\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments
May 8, 2026Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer +1Learning-Augmented Algorithms Adversarial Training
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Federated Learning Heterogeneous Edge Devices
Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
May 8, 2026Hao Chen, Zavareh BozorgaslWireless Resource Allocation Federated Learning
Modulated learning for private and distributed regression with just a single sample per client device
May 8, 2026Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1Federated Learning Physical Activities
Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
May 8, 2026Hong Chen, Pengcheng Wu, Yuanguo Lin +4Federated Learning Cross-Entropy Losses
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Heterogeneous Federated Learning Federated Learning
Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
May 7, 2026Minheng Chen, Chao Cao, Jing Zhang +2Functional Networks Atlas
Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
May 7, 2026Mianyong Ding, Maximilian Knoll, Semi Harrabi +7Federated Learning Lesion Segmentation
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
May 7, 2026Su Zhang, Junfeng Guo, Heng HuangFederated Learning Privacy-Preserving Framework
CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
May 7, 2026Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2Internet Of Thing Intrusion Detection
The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models
May 7, 2026Flavio Nicoletti, Chenxiao Ma, Enrico Ventura +2Score-Based Diffusion Model Distributional Learning
Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity
May 7, 2026Florian A. D. Burnat, Brittany I. DavidsonLarge Language Model Evaluation Heterogeneity
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
May 7, 2026Wentao Yu, Bo Han, Jie Yang +1Federated Graph Learning Federated Learning
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
May 7, 2026Xinghao Wu, Jianwei Niu, Guogang Zhu +3Heterogeneous Federated Learning Heterogeneity
VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
May 7, 2026Mohamed Lakas, Mohamed Amine FerragFederated Learning Internet Of Thing
Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio
May 7, 2026Ziqing Wen, Zhouyang Liu, Jiahuan Wang +4Adam Benchmarking Large Language Models
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
May 7, 2026Myoungjun Kim, Sangwoo Park, Yoseob Han +1Federated Learning Low-Rank Adaptation
Enabling Federated Inference via Unsupervised Consensus Embedding
May 7, 2026Yui Hashimoto, Takayuki Nishio, Yuichi Kitagawa +1Federated Learning Privacy-Preserving Framework
FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings
May 7, 2026Quang-Huy Nguyen, Jiaqi Wang, Wei-shinn KuFedavg
MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
May 6, 2026Xuwei Yang, David B. Emerson, Fatemeh Tavakoli +1Federated Reinforcement Learning Multi-Agent Reinforcement Learning
Hypergraph Generation via Structured Stochastic Diffusion
May 6, 2026Christopher NemethHypergraphs Generative Models
Graph-SND: Sparse Aggregation for Behavioral Diversity in Multi-Agent Reinforcement Learning
May 6, 2026Shawn RayMulti-Agent Reinforcement Learning Graph Representations
A Simulated Federated Analysis of MS-Induced Brain Lesions
May 6, 2026Evelyn Trautmann, Joël Federer-Gsponer, Markus C. Elze +1Federated Learning Multiple Sclerosis
Federated Learning for Early Prediction of EV Charging Demand
May 6, 2026Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis +2Electric Vehicles Electricity Price Forecasting
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
May 6, 2026Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7Federated Learning Candidate Label Distributions
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
May 6, 2026Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2Federated Learning Fairness-Aware Learning
HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
May 6, 2026Xinyi Li, Ming Li, Lu Bai +5Text-Attributed Graph Heterogeneity
FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling
May 6, 2026Guangyi Zhang, Yi Dai, Yiyun He +1Federated Learning Single-Cell Rna Sequencing
Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data
May 5, 2026Surya Jayakumar, Kieran Sullivan, John McLaughlin +2Agent-Based Model Carbon Emissions
A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
May 5, 2026Dario Filatrella, Ragnar Thobaben, Mikael SkoglundFederated Learning Generalization Bounds

May 12, 2026 · eess.IVJ/K move · Enter open · S save

On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning

Kexin Zhang, Lixin Li, Yuna Yan, Xin Zhang+4

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China · DecoreX Intelligent Technologies Co., Ltd., Xi’an 710075, China · Samsung AI Center, Cambridge CB1 2JH, UK · 208th Research Institute of China North Industries Group Corporation, Beijing 102202, China · Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA

The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspection, and emergency response. However, transmitting high-volume image data from UAVs to ground stations faces significant challenges due to limited bandwidth and stringent privacy requirements. To address these issues, a Semantic Communication (SC) framework based on Federated Learning (FL) is proposed for efficient and privacy-preserving image transmission. A Swin Transformer-based Semantic Communication (STSC) architecture is designed to extract multi-scale semantic features under constrained bandwidth conditions. Dedicated communication and computing nodes are deployed on UAVs to enhance real-time coverage and flexibility. Meanwhile, a FL mechanism enables global model training across distributed devices without sharing raw data, thus preserving user privacy. Simulation experiments conducted on the CIFAR-10 dataset demonstrate that the proposed STSC framework achieves at least 5.7 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to DeepJSCC baselines, while also showing superior convergence and generalization performance. The framework effectively integrates UAV-assisted deployment with SC and privacy protection, offering a practical solution for bandwidth-constrained image transmission in low-altitude networks.

Unmanned Aerial Vehicles Semantic Communications Federated Learning Swin Transformer Architectures

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