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.

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
May 1, 2026Luru Jing, Cong Cong, Yanyuan Chen +1Computational Pathology Dataset Distillation
School of Computer Science, Peking University, Beijing, China · Center for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia · School of Data Science, University of Virginia, Charlottesville, VA, USA.
Federated Learning with Hypergradient-based Online Update of Aggregation Weights
May 1, 2026Ayano Nakai-Kasai, Tadashi WadayamaFederated Learning Aggregation Non-IID Federated Learning
Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Apr 30, 2026Zhiqiang Kou, Junxiang Wu, Wenke Huang +8Multi-Label Classification Federated Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · Wuhan University, China +3
FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning
Apr 30, 2026Mahad Ali, Laura J. BrattainNon-IID Federated Learning Clustered Federated Learning
University of Central Florida, Orlando FL 32816, USA
SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
Apr 29, 2026Yimeng Shan, Zhaorui Zhang, Sheng Di +3Communication-Efficient Distributed Training LLM Fine-Tuning
Hong Kong Polytechnic University Hong Kong · Argonne National Labratory USA · University of California, Merced USA +1
Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
Apr 29, 2026Martina Pavan, Matteo Caligiuri, Francesco Barbato +1Class-Imbalanced Learning Synthetic Data Augmentation
University of Padova, Padova (PD), Italy · NIDEK Technologies Srl, Albignasego (PD), Italy
Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
Apr 28, 2026Shuchen Zhu, Zhengyang Huang, Yuqi Xu +1Non-IID Federated Learning Federated Learning
Peking University · Beihang University
When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Apr 27, 2026Chamani Shiranthika, Parvaneh SaeediDomain Adaptation Medical Image Classification
School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
Apr 27, 2026Yutong He, Zhengyang Huang, Jiahe Geng +1Communication-Efficient Distributed Training Gradient Compression
Peking University · Beihang University
Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization
Apr 24, 2026Hefeng Zhou, Xuan Liu, Sicheng Chen +7Cross-Modal Learning Multimodal Federated Learning
Shanghai Jiao Tong University · Hohai University · Shenzhen Univ of Advanced Technology
Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints
Apr 21, 2026Konstantinos Ziliaskopoulos, Alexander VinelPredict-Then-Optimize Learning to Optimize
Auburn University
Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Apr 21, 2026Yuhan Hu, Xiaolei FangRUL Estimation Predictive Maintenance
Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
Apr 21, 2026Tao Fan, Guoqiang Ma, Yuanfeng Song +3Privacy-Preserving Language Models LLM Fine-Tuning
Hong Kong University of Science and Technology, Hong Kong, China · WeBank Co., Ltd, Shenzhen, China · Hong Kong Polytechnic University, Hong Kong, China
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
Apr 17, 2026Xingyan Chen, Tian Du, Changqiao Xu +4Federated Learning Heterogeneous Federated Learning
Beijing Key Laboratory of MEMS Technology and Device Reliability for Industrial Internet, the School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China · School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, P. R. China · Institute of Artificial Intelligence and the School of Computer Science, Beihang University, Beijing 100191, China, and with the SKLSDE +3
Fed3D: Federated 3D Object Detection
Apr 17, 2026Suyan Dai, Chenxi Liu, Fazeng Li +13D Object Detection Federated Learning
School of Automation Science and Enginnering, South China University of Technology, Guangzhou 510641, P.R.China. · State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China.
FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems
Apr 16, 2026Wenhao Wang, Haoting Shi, Mengying Yuan +7Benchmark Design GUI Agents
Zhejiang University · Shanghai Jiao Tong University · Multi-Agent Governance & Intelligence Crew (MAGIC) +2
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
Mar 22, 2026Zihan Fang, Qianru Wang, Haonan An +4Mixture-of-Experts Language Models Federated Learning Aggregation
Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China · School of Computer Science and Technology, Xidian University, Xi’an, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong, China +1
Decentralized Federated Learning by Partial Message Exchange
Mar 2, 2026Shan Sha, Shenglong Zhou, Xin Wang +2Communication-Efficient Distributed Training Federated Learning
School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, China · Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, U.K.
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
Feb 24, 2026Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1Cross-Modality Medical Image Segmentation Data Augmentation
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway. · Central Staff, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI
Feb 16, 2026Qingqian Yang, Hao Wang, Sai Qian Zhang +6Federated Learning Aggregation Non-IID Federated Learning
Stevens Institute of Technology · Hoboken, NJ, USA · New York University +9
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
Feb 13, 2026Ayse Tursucular, Ayush Mohanty, Nazal Mohamed +1Federated Learning Nonlinear System Identification
Georgia Institute of Technology Atlanta, GA, USA
An Adaptive Differentially Private Federated Learning Framework
Feb 6, 2026Jin Wang, Hui Ma, Yajun Zhang +4Non-IID Federated Learning Differentially Private Federated Learning
Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi 830017, China · College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China · Jilin University, Changchun 130012, China
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Sep 5, 2025Jiaojiao Zhang, Yizhao Fan, Yuqi Xu +1Communication-Efficient Distributed Training Non-IID Federated Learning
Intelligent Computing Research Center, Great Bay University, Dongguan, China · Center for Machine Learning Research, Peking University, Beijing, China
FAIRVAR: Fair Federated Learning via Variance Regularization
Aug 16, 2025Zahra Kharaghani, Ali Dadras, Tommy LöfstedtAlgorithmic Fairness Federated Learning
Department of Computing Science, Umeå University, Umeå, Sweden · Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
Aug 16, 2025You Hak Lee, Keming Fan, Xiaofan Yu +4Compute-in-Memory Federated Learning Aggregation
University of California San Diego, USA
Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
Jun 27, 2025Ying Zhang, Yu Zhao, Xuhui Sui +5Multimodal Federated Learning Missing-Modality Learning
College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
May 28, 2025Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7Federated Learning Aggregation Differentially Private Stochastic Gradient Descent
German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye · Medical Research Group, MLCommons, San Francisco, CA, USA +2
Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation
May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning Aggregation Independent Component Analysis
University of Toronto · National University of Singapore · Rutgers University
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
May 16, 2025Shudi Weng, Chao Ren, Ming Xiao +1Federated Learning Heterogeneous Federated Learning
Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 11428 Stockholm, Sweden
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
Sep 9, 2024Jianyi Zhang, Hao Frank Yang, Ang Li +5Multimodal Federated Learning Non-IID Federated Learning
Duke University · Johns Hopkins University · University of Maryland College Park +1

May 1, 2026, cs.CVJ/K move · Enter open · S save

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration

Luru Jing, Cong Cong, Yanyuan Chen, Yongzhi Cao

School of Computer Science, Peking University, Beijing, China · Center for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia · School of Data Science, University of Virginia, Charlottesville, VA, USA.

Computational Pathology Dataset Distillation Whole-Slide Image Analysis Federated Learning Heterogeneous Federated Learning

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Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-world deployments face heterogeneity arising from diverse multiple instance learning (MIL) architectures and heterogeneous feature extractors across institutions. We propose FedHD, a novel FL framework that performs local Gaussian-mixture feature alignment tailored for WSI analysis. Instead of exchanging model parameters, each client independently distills semantically rich synthetic feature representations aligned with the distribution of real WSIs. To preserve diagnostic diversity, FedHD adopts a one-to-one distillation strategy, generating a synthetic counterpart for each real slide to avoid over-compression. During federation, a curriculum-based integration strategy progressively incorporates cross-site synthetic features into local training once performance plateaus. Furthermore, an optional interpretation module reconstructs pseudo-patches from synthetic embeddings, enhancing transparency. FedHD is architecture-agnostic, privacy-preserving, and supports personalized yet collaborative training across diverse institutions. Experiments on TCGA-IDH, CAMELYON16, and CAMELYON17 show that FedHD consistently outperforms state-of-the-art federated and distillation baselines.