Federated Learning

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ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Federated Learning Ensemble
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
May 22, 2026Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly +6Whole-Slide Images Computational Pathology
PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
May 22, 2026Anisa Halimi, Liubov Nedoshivina, Kieran Fraser +1Federated Learning Privacy-Preserving Framework
Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette
May 22, 2026Koffka KhanFederated Learning Heterogeneous Edge Devices
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
May 21, 2026Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin +3Random Forest Heterogeneity
FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
May 21, 2026Rachid HedjamFederated Learning Privacy-Preserving Framework
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
May 21, 2026Tianyun Zhang, Zhen Yang, Haozhao Wang +2Federated Learning Image Clustering
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
May 21, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2Federated Learning Quantitative Idea Divergence
FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G
May 20, 2026Amin Farajzadeh, Melike Erol-KantarciWireless Resource Allocation Federated Reinforcement Learning
Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
May 20, 2026Fan Zhang, Simon Deltadahl, Majid Lotfian Delouee +10Federated Learning Fedavg
Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
May 20, 2026Chaimaa Medjadji, Sylvain Kubler, Yves Le Traon +3Federated Learning Knowledge Distillation
CRAFT: Conflict-Resolved Aggregation for Federated Training
May 20, 2026Ziqi Wang, Qiang Liu, Nils ThuereyFederated Learning Fairness-Aware Learning
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
May 20, 2026Penglin Dai, Fulian Li, Xincao Xu +3Federated Learning Fedavg
Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment
May 20, 2026Shuaida He, Liwen Chen, Long FengLow-Rank Adaptation Federated Learning
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
May 20, 2026Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-DoudaneElectric Vehicles Federated Learning
A Typed Tensor Language for Federated Learning
May 20, 2026Theofilos Mailis, Kalliopi-Christina Despotidou, Konstantinos Filippopolitis +6Federated Learning Tensor Programs
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
May 20, 2026Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1Federated Learning Fairness-Aware Learning
LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
May 20, 2026Yassine Maziane, Ammar Mahran, Artavazd Maranjyan +1Distributed Optimization Federated Learning
Distributed Direct Preference Optimization
May 20, 2026Zhanhong JiangDirect Preference Optimization Federated Reinforcement Learning
Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management
May 19, 2026Takato YasunoPredictive Maintenance Time-To-Event Data
Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
May 19, 2026Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi +1Federated Learning Exact Unlearning
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning
May 19, 2026T. Tony Cai, Yicheng LiDifferential Privacy Federated Learning
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
May 19, 2026Ilya Levin, Maksim Shuklin, Eric Moulines +2Stochastic Approximation Multiplier Bootstrap
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
May 19, 2026Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2Federated Learning Representation-Level Certification
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 Edge Devices
FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
May 19, 2026Yudan Ren, Pengcheng Shi, Zihan Ma +2Human Brain Networks Brain Magnetic Resonance Imaging
Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +2Medical Image Classification Latent Confounders
What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Models
May 18, 2026Payal Chandak, Victoria Alkin, David Wu +11Pluralistic Alignment Clinical Decision Support
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
May 18, 2026Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat +1Federated Learning Mental Health
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
May 18, 2026Arnab Auddy, Xiangni Peng, Subhadeep PaulFederated Learning Differential Privacy
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
May 18, 2026Yaorong Huang, Jingtao Luo, Xuechao WangHeterogeneous Edge Devices Federated Reinforcement Learning
Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
May 18, 2026Aakar Mathur, Mohammed Ruknuddin, Ashish GuptaVariational Quantum Circuits Federated Learning
Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
May 18, 2026M Yashwanth, Arunabh Singh, Ashok Nayak +2Federated Learning Participation
Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
May 17, 2026Vedant Waykole, Haroon R. LoneFederated Learning Heterogeneous Edge Devices
SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
May 17, 2026Qingnan Ren, Shun Zou, Shiting Huang +11Coding Agents Software
Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings
May 17, 2026Wenxuan XiaoQuantizer Embedding Dimension
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
May 17, 2026Yuhan Xie, Chen Lyu, Jingrong HuangFederated Learning Fairness-Aware Learning
Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
May 17, 2026S. Tazili, A. Mansour, M. Y. ChkouriIntrusion Detection Cybersecurity
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
May 16, 2026Xiaolu Chen, Chenghao Huang, Yanru Zhang +1Photovoltaic Federated Learning
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 +1Heterogeneous Federated Learning Mixture-of-Experts Models
Multiscale Supervised Unbalanced Optimal Transport Flow Matching
May 15, 2026Qiangwei Peng, Lezhi Chen, Peijie ZhouSingle-Cell Rna Sequencing Optimal Transport
Federated Imputation under Heterogeneous Feature Spaces
May 15, 2026Imane Hocine, Chaimaa Medjadji, Sylvain Kubler +2Federated Learning Imputation
Centralized vs Decentralized Federated Learning: A trade-off performance analysis
May 15, 2026Chaimaa Medjadji, Guilain Leduc, Sylvain Kubler +1Federated Learning Heterogeneous Edge Devices
Practical Validity Conditions for Byzantine-Tolerant Federated Learning
May 15, 2026Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1Byzantine Attacks Federated Learning
SEED: Targeted Data Selection by Weighted Independent Set
May 15, 2026Yuan Zhang, Lifeng Guo, Junwen Pan +5Geometry-Aware Uncertainty Coresets Heterogeneity
Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
May 15, 2026Sergei Chuprov, Richard D. Lange, Leon Reznik +3Metacognition Artificial Intelligence Systems
Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
May 15, 2026Hangyu WuFederated Learning Cortex
Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
May 14, 2026Andras Ferenczi, Sutapa Samanta, Dagen Wang +1Federated Learning Byzantine Attacks
Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions
May 14, 2026Sanja Karilanova, Subhrakanti Dey, Ayça ÖzçelikkaleSpiking Neural Networks Heterogeneous Federated Learning
BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring
May 14, 2026Zixuan Shu, Tiancheng Cao, Hen-Wei HuangFederated Learning Knowledge Distillation
FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology
May 14, 2026Fengyi Zhang, Junya Zhang, Wenzhuo SunFederated Learning Computational Pathology
Head Forcing: Long Autoregressive Video Generation via Head Heterogeneity
May 14, 2026Jiahao Tian, Yiwei Wang, Gang Yu +1Autoregressive Video Diffusion Autoregressive Video Generation
Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic
May 14, 2026Leo Muxing Wang, Pengkun Yang, Lili SuFederated Reinforcement Learning Soft Actor-Critic
On the Burden of Achieving Fairness in Conformal Prediction
May 14, 2026Ziang Gao, Pengqi Liu, Archer Yi Yang +3Conformal Prediction Conformal Calibration
Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
May 14, 2026Kexin Chu, Dawei Xiang, Wei ZhangLarge Language Model Routing Large Language Model Calls
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
May 13, 2026Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3Federated Learning Privacy-Preserving Framework
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
May 13, 2026Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos +4Federated Learning Homomorphic Encryption
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
May 13, 2026Huan Wang, Jun Shen, Haoran Li +6Heterogeneous Federated Learning Federated Learning
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
May 13, 2026Ammar Mahran, Artavazd Maranjyan, Peter RichtárikStochastic Gradient Descent Distributed Optimization
Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
May 12, 2026Kamel Abdous, Nairouz Mrabah, Mohamed BouguessaText-Attributed Graph Heterogeneity

May 23, 2026 · cs.LGJ/K move · Enter open · S save

ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning

Karan Sharma, Aditya Tripathi, Rahul Mishra, Tapas Kumar Maiti

Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and centralized aggregation, which fails when hospitals have unequal compute resources. We propose capacity-aware coordination: measure each hospital's throughput, assign capacity-appropriate architectures (MobileNetV3-Small, EfficientNet-B0, ResNet-50), and combine predictions via weighted ensemble. Weak and strong hospitals can participate without forcing uniform architectures. We separate on-chain policy from off-chain learning. A Solidity contract stores hospital registration, benchmark hashes, metrics, and weights. Hospitals train locally and submit only hashes and scalars (not parameters). Weighted ensemble inference is computed off-chain. Experiments on PneumoniaMNIST and DermaMNIST (5 seeds, 3 non-IID levels) show our method achieves lower or equal calibration error versus equal-weight ensemble and competitive accuracy versus FedAvg, FedProx, and FedMD. Communication overhead is 224 bytes per round, a reduction of over 912,000x compared to FedAvg.

Federated Learning Ensemble Privacy-Preserving Framework Heterogeneous Edge Devices Medical Imaging

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