Heterogeneous Federated Learning

Latest papers 214

38 papers in the last four weeks, up 23% on the four weeks before. 0.3% of all new papers.

MoE Enhanced Federated Learning for Spatiotemporal Prediction
Jun 9, 2026Zhehao Dai, Xiao Han, Zhaolin Deng +4Intelligent Transportation Systems Mixture of Experts
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou · Jilin University, Jilin · City University of Hong Kong, Hong Kong SAR China
AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments
Jun 6, 2026Yan Wang, Ziyi Gao, Rui WangLLM Fine-Tuning Asynchronous Federated Learning
Department of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
Jun 5, 2026Lin Qiang, Sun Xiaoyan, Hu Yao +1Client Selection in Federated Learning Federated Learning
1Jiangnan University · 2The Hong Kong Polytechnic University
Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
Jun 4, 2026Su Wang, Mung Chiang, H. Vincent PoorCommunication-Efficient Distributed Training Clustered Federated Learning
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA · Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
FederatedSkill: Federated Learning for Agentic Skill Evolution
Jun 2, 2026Jingbo Yang, Guanyu Yao, Yang Zhang +3LLM Agent Self-Improvement LLM Agent Skill Learning
UC Santa Barbara · MIT-IBM Watson AI Lab · Cisco Research
Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection
Jun 2, 2026Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1Hierarchical Federated Learning Non-IID Federated Learning
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Jun 1, 2026Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella SunFederated Learning Aggregation Privacy-Preserving ML
University of Tennessee, Knoxville, USA · Oak Ridge National Laboratory, USA
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Jun 1, 2026Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1Non-IID Federated Learning Federated Learning
CITIC, Universidade da Coruña, A Coruña, Spain
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Jun 1, 2026Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1Reinforcement Learning Deep Q-Learning
Department of Computer Science, Kennesaw State University, Marietta, GA, 30060 USA · Department of Computer Science, San Francisco State University, San Francisco, CA, 94132 USA · Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204 USA
FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment
Jun 1, 2026Nazmus Shakib Shadin, Aaron Cummings, Xinyue Zhang +1Non-IID Federated Learning Multi-Teacher Knowledge Distillation
Department of Computer Science, Kennesaw State University, Marietta, GA, 30060 USA
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
May 30, 2026Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri +2Non-IID Federated Learning Federated Learning
University of Naples "Federico II", Italy · Queen Mary University of London, United Kingdom · Brandenburg University of Technology Cottbus-Senftenberg, Germany +2
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
May 28, 2026Ambreen Aslam, Maaz Hassan, Bibi Zahra +1Federated Learning Aggregation Network Intrusion Detection
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Fairness-Aware Federated Learning with Trajectory Shapley Value
May 28, 2026Daniel Kuznetsov, Ziqi WangFederated Learning Aggregation Algorithmic Fairness
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay, 4 Av. des Sciences, 91190 Gif-sur-Yvette, France. · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany.
Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets
May 28, 2026Prasanjit Dubey, Xiaoming HuoFederated Learning Aggregation Communication-Efficient Distributed Training
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
May 27, 2026Yuchen Hou, Yongshan Chen, Zhuowen Zou +4RL Control Federated Learning Aggregation
Northeastern University · University of California, Irvine · The George Washington University
Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
May 27, 2026Yuting Ma, Lechao Cheng, Xiaohua XuVLM Adaptation Vision-Language Alignment
School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China · School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China.
FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
May 27, 2026Jun Bai, Ziyang Song, Yue LiSynthetic Data Generation Conditional Variational Autoencoder
McGill University Mila - Quebec AI Institute Montreal, Canada · Ohio University Athens, United States
Pattern Recognition Tasks with Personalized Federated Learning
May 27, 2026Md. Arifur Rahman, Isha Das, Mushfiqur Rahman Abir +4Image Classification Federated Learning
College of Graduate and Professional Studies, Trine University, Angola, IN 46703, United States. · Network Communication and IoT Lab, Chittagong University of Engineering and Technology, Chittagong, Bangladesh. · Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh. +4
Separate Aggregation of Split Network for Personalized Federated Learning
May 26, 2026Yunseok Kang, Jaeyoung SongFederated Learning Aggregation Split Federated Learning
Department of Electronics Engineering, Pusan National University, Republic of Korea
Personalized Federated Learning by Energy-Efficient UAV Communications
May 24, 2026Shiqian Guo, Jianqing Liu, Beatriz LorenzoEnergy-Efficient ML Client Selection in Federated Learning
Department of Computer Science, North Carolina State University, Raleigh, NC 27606 USA · University of Massachusetts, Amherst, MA 01003 USA
Hardware-Aware Federated Learning for Speech Emotion Recognition
May 23, 2026Beyazit Bestami Yuksel, Emrah DikbiyikCommunication-Efficient Distributed Training Client Selection in Federated Learning
Computer Engineering Istanbul Technical University Istanbul, Turkey · Department of Computer Technologies Istanbul University-Cerrahpaşa Istanbul, Turkey
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Federated Learning Aggregation Federated Learning
DA-IICT Gandhinagar, India · IIT Patna Patna, India
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
May 21, 2026Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin +3Federated Learning Aggregation Cancer Survival Prediction
Institute for Predictive Deep Learning in Medicine and Healthcare, Justus Liebig University Gießen, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Department of Medical Informatics, University Medical Center Göttingen, Germany +1
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
May 21, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2Federated Learning Aggregation Non-IID Federated Learning
Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain
Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
May 20, 2026Chaimaa Medjadji, Sylvain Kubler, Yves Le Traon +3Communication-Efficient Distributed Training Federated Knowledge Distillation
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg · Blekinge Institute of Technology, Sweden · ADSLabs, Umea University, Sweden
CRAFT: Conflict-Resolved Aggregation for Federated Training
May 20, 2026Ziqi Wang, Qiang Liu, Nils ThuereyFederated Learning Aggregation Gradient Interference
Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. · School of Computation, Information and Technology, Technical University of Munich, Garching, Germany. · Munich Center for Machine Learning, Munich, Germany.
Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment
May 20, 2026Shuaida He, Liwen Chen, Long FengLLM Fine-Tuning Low-Rank Adaptation
School of Computing & Data Science, The University of Hong Kong
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
May 19, 2026Wei-Bin Kou, Guangxu Zhu, Ming Tang +4Federated Learning Heterogeneous Federated Learning
Yinwang Intelligent Technology Co. Ltd., Shenzhen, China
FedSDR: Federated Self-Distillation with Rectification
May 18, 2026Ziheng Ren, Zhanming Shen, Hao Wang +2LLM Fine-Tuning Hallucination in Language Models
Beijing University of Aeronautics and Astronautics · Zhejiang University · Stevens Institute of Technology +1
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
May 15, 2026Van-Tuan Tran, Hong-Hanh Nguyen-Le, Marco Ruffini +1Expert Load Balancing Sparse Mixture-of-Experts
School of Computer Science and Statistics, Trinity College Dublin, Ireland · School of Computer Science, University College Dublin, Ireland

Jun 9, 2026, cs.LGJ/K move · Enter open · S save

MoE Enhanced Federated Learning for Spatiotemporal Prediction

Zhehao Dai, Xiao Han, Zhaolin Deng, Zijian Zhang+3

Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou · Jilin University, Jilin · City University of Hong Kong, Hong Kong SAR China

Intelligent Transportation Systems Mixture of Experts Cross-Domain Transfer Learning Spatiotemporal Forecasting Federated Learning Heterogeneous Federated Learning

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Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor deployment and uneven urban development. Cross-city knowledge transfer has thus attracted increasing attention, enabling data-rich cities to assist data-scarce ones. However, centralized approaches raise privacy concerns, while existing federated methods struggle with pronounced spatiotemporal heterogeneity across cities. To address these challenges, we propose MoE-FedTP, a personalized federated cross-city spatiotemporal prediction framework based on lightweight Mixture-of-Experts (MoE) networks. MoE-FedTP first employs spatiotemporal neural networks to extract features from both source and target cities, then introduces a set of expert networks derived from different source cities through partial parameter sharing. A gating mechanism dynamically fuses the experts to capture diverse traffic dynamics, achieving fine-grained modeling of urban heterogeneity while preserving privacy. Experiments on four real-world traffic datasets show that MoE-FedTP consistently outperforms state-of-the-art cross-city and federated learning baselines, demonstrating its effectiveness in enhancing prediction accuracy for data-scarce cities.