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.

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication
Aug 15, 2026Lin Yin, Tiejun Lv, Weicai Li +2Federated Learning Federated Learning Aggregation
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China · Center for Target Cognition Information Processing Science and Technology, and the Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, both at Beijing Information Science and Technology University, Beijing, China
Global Federated Learning Strategies for Building Efficient Personalized Models
Aug 15, 2026Seongyoon KimFederated Learning Heterogeneous Federated Learning
한국과학기술원 Korea Advanced Institute of Science and Technology 산업및시스템공학과
PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity
Aug 15, 2026Jindi Wu, Qun LiFederated Learning Quantum Machine Learning
School of computing DePaul University Chicago, USA · Computer science department William & Mary Williamsburg, USA
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
Aug 14, 2026Keya Patel, Sajib Mistry, Sheik Fattah +4Federated Learning Neural Architecture Search
School of Electrical Engineering, Computing and Mathematical Sciences Curtin University, Perth, Australia · King Fahd University of Petroleum and Minerals, Saudi Arabia · Umeå University, Umeå, SE-90187, Sweden
MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity
Aug 14, 2026Adiba Orzikulova, Dong Min Kim, Jaehong Yoon +1Multimodal Federated Learning Federated Learning
KAIST · NTU Singapore
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
Aug 12, 2026Mirko Konstantin, Stefan Zachow, Anirban MukhopadhyayFederated Learning Aggregation Decentralized Learning
Zuse Institute Berlin (ZIB) Takustraße 7 14195 Berlin, Germany · Technical University of Darmstadt Karolinenplatz 5 64289 Darmstadt, Germany
Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks
Aug 12, 2026Qasim Zia, Saide Zhu, Haoxin Wang +2Hierarchical Federated Learning Digital Twins
Georgia State University, Atlanta, GA, 30303 · Penn State Berks, Reading, PA, 19610
FedCGR: Federated Cross-Domain Generative Recommendation
Aug 11, 2026Zhuodong Liu, Hugen Lv, Xiangyu Li +2Recommender Systems Generative Recommendation
Beijing Jiaotong University China · Shanghai Jiao Tong University China · University of Malaya Malaysia +1
SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks
Aug 10, 2026Yue Xia, Tayyebeh Jahani-Nezhad, Mayank Bakshi +1Federated Learning Aggregation Low-Rank Adaptation
Technical University of Munich Munich, 80333 Germany · Technische Universität Berlin Berlin, 10587 Germany · Northern Arizona University Flagstaff, AZ 86011 USA
FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients
Aug 10, 2026Bostan Khan, Masoud DaneshtalabDynamic Neural Networks Communication-Efficient Distributed Training
Mälardalen University, Västerås, Sweden
Multimodal Federated Learning under Dual-Axis Modality Missingness
Aug 10, 2026Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5Multimodal Federated Learning Missing-Modality Learning
1KAIST · 2CUHK · 3HKUST
Label Granularity Skew in Federated Learning with Hierarchical Image Classification
Aug 10, 2026Jaeheon Kim, Hokeun Kim, Bong Jun ChoiHierarchical Classification Federated Fine-Tuning
School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea · School of Computing and Augmented Intelligence, Arizona State University, Tempe, United States
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
Aug 10, 2026Jae Ho Chang, Arnab Auddy, Subhadeep PaulEmpirical Bayes Federated Learning
Department of Statistics, The Ohio State University
Sheaf-Based Federated Representation Learning
Aug 8, 2026Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino +4Federated Learning Heterogeneous Federated Learning
Sapienza University of Rome, Italy
FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity
Aug 7, 2026Deepank Girish, Yi Hao Chan, Yubin Zheng +2Functional Connectivity Medical Diagnosis
Nanyang Technological University · Shanghai Jiao Tong University · Indian Institute of Technology Ropar
Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning
Aug 7, 2026Alireza MoayedikiaNon-IID Federated Learning Federated Learning
Department of Business Technology and Entrepreneurship Swinburne University of Technology Melbourne, Australia
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
Aug 6, 2026Grigory MalinovskyByzantine-Robust Federated Learning Communication-Efficient Distributed Training
King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia
FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT
Aug 6, 2026Mohammad Hosssein Gholamrezazadeh, Ahmadreza MontazerolghaemAhmadreza MontazerolghaemIoT Intrusion Detection Cybersecurity
Faculty of Computer Engineering University of Isfahan Isfahan, Iran
FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
Aug 4, 2026Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh +1Clinical Outcome Prediction Federated Learning
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne, Parkville, Australia · Department of Computer Science School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar, India
AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning
Aug 4, 2026Shengyang Li, Yiting Dong, Liuyang Song +5Spiking Neural Networks Cross-Architecture Knowledge Distillation
School of Software and Microelectronics, Peking University, Beijing, China · School of Computer Science, Peking University, Beijing, China · Institute for Artificial Intelligence, Peking University, Beijing, China
CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning
Aug 3, 2026Zijian Jiang, Chaoli Sun, Handing Wang +1Non-IID Federated Learning Representation Alignment
School of Computer Science and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, U.K. · School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China · School of Artificial Intelligence, Xidian University, Xi’an 710071, China +1
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Aug 3, 2026Seongyoon Kim, Boryeong Cho, Jihwan Oh +2Pairwise Preference Learning Reward Modeling
Institute of Engineering Research, Korea University Seoul, Republic of Korea · Kim Jaechul Graduate School of AI, KAIST Seoul, Republic of Korea · Industrial Management Engineering, Korea University Seoul, Republic of Korea
FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity
Aug 2, 2026Ganghyeon Lee, Inha Lee, Junhee Lee +3Depth Estimation Non-IID Federated Learning
Ulsan National Institute of Science and Technology (UNIST), Republic of Korea
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Aug 1, 2026Yinlin Zhu, Di Wu, Yi Zhang +5Multimodal Federated Learning Federated Graph Learning
Sun Yat-sen University, Guangzhou, China1 · Shandong University, Weihai, China2 · Beijing Institute of Technology, Beijing, China3
Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients
Jul 31, 2026Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5Federated Learning Aggregation Federated Fine-Tuning
The Hong Kong Polytechnic University · OceanBase, Ant Group · School of Management, Xi’an Jiaotong University +3
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Jul 30, 2026Michael Ben Ali, Imen Megdiche, André Péninou +1Privacy-Preserving ML Clustered Federated Learning
UT3, IRIT, CNRS · INU Champollion, ISIS, IRIT, CNRS · UT2J, IRIT, CNRS +1
FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning
Jul 29, 2026Zhaoyang Ma, Zhihao Wu, Xin Gao +3Federated Learning Aggregation Federated Learning
Beijing Jiaotong University Beijing, China · Nanyang Technological University Singapore, Singapore
FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA
Jul 29, 2026Donghang Duan, Xu Zheng, Lizong Zhang +2Federated Learning Aggregation LLM Routing
University of Electronic Science and Technology of China · 2Zhejiang University
Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests
Jul 28, 2026Mohammad Tajabadi, Dominik HeiderLearning with Missing Data Decentralized Learning
Institute of Medical Informatics, University of Münster, Münster, Germany
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
Jul 27, 2026Qingxiang Liu, Anqi Liang, Heng Wang +1Domain Generalization Causal Inference
The Hong Kong University of Science and Technology (Guangzhou) · Shanghai Jiao Tong University · Harbin Engineering University

Aug 15, 2026, cs.AIJ/K move · Enter open · S save

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

Lin Yin, Tiejun Lv, Weicai Li, Xi Yu+1

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China · Center for Target Cognition Information Processing Science and Technology, and the Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, both at Beijing Information Science and Technology University, Beijing, China

Federated Learning Federated Learning Aggregation Semantic Communication Heterogeneous Federated Learning Multi-Task Learning

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Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). However, pushing topology-agnostic aggregation into heterogeneous, multi-task environments creates a fundamental bottleneck: it drives negative transfer and overconsensus bias (OCB). This paper introduces a personalized DSC framework that cuts off this cross-task interference. At the node level, a policy-driven multi-path routing mechanism separates task-specific features from shared representations to preserve local fidelity. Across the network, we deploy a "communicationwhile- aggregation" protocol. It calibrates a column-stochastic consensus matrix using task affinities. This limits the system to absorbing complementary knowledge while actively blocking mismatched parameter updates. To bound the convergence, we derive a unified Lyapunov drift analysis. We reveal a strict Ushaped trade-off: deeper topological mixing reduces variance but amplifies structural OCB. Resolving this tension yields a closed-form expression for the optimal aggregation depth. We evaluate the proposed framework on NYU-v2, where the results reveal a clear trade-off between insufficient aggregation and excessive topological mixing. At the analytically derived optimal aggregation depth, our method achieves a 4.77% global relative improvement over the no-aggregation baseline and outperforms decentralized FedAvg, FedAMP, and heuristic max aggregation. We further evaluate the framework on Taskonomy and imperfect wireless links to examine the effects of network-size variation and wireless-link reliability.