Federated Learning

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91 papers in the last four weeks, up 176% on the four weeks before. 0.9% of all new papers.

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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
Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
Apr 21, 2026Yi Zhao, Di Yuan, Tao Deng +2Wireless Communications Federated Learning
Department of Information Technology, Uppsala University, 75105 Uppsala, Sweden · School of Computer Science and Technology, Soochow University, Suzhou 215006, China · Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China +1
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
Apr 21, 2026Harekrushna Sahu, Pratik Jawanpuria, Pranay SharmaOnline Convex Optimization Non-IID Federated Learning
CMInDS, IIT Bombay, India
Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Apr 21, 2026Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4Privacy-Preserving ML Federated Learning
DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
Apr 21, 2026Isaiah Thompson, Tanmay Sen, Ritwik BhattacharyaFederated Learning Anomaly Detection
Department of Mathematical Sciences University of Texas at El Paso El Paso, TX 79968, USA · SQC & OR Unit Indian Statistical Institute Kolkata, 700118, India
Federated Rule Ensemble Method in Medical Data
Apr 20, 2026Ke Wan, Kensuke Tanioka, Toshio ShimokawaInterpretable ML Privacy-Preserving ML
Department of Medical Data Science Wakayama Medical University · Department of Biomedical Sciences and Informatics Doshisha University · Department of Biostatistics Wakayama Medical University
Federation over Text: Insight Sharing for Multi-Agent Reasoning
Apr 18, 2026Dixi Yao, Tahseen Rabbani, Manzil Zaheer +1Multi-Agent LLM Systems Multi-Agent Reasoning
University of Chicago · Google DeepMind
FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction
Apr 17, 2026Seerat Kaur, Sukhjit Singh Sehra, Dariush EbrahimiPrivacy-Preserving Language Models Traffic Flow Estimation
Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, Canada
SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models
Apr 17, 2026Noor Islam S. Mohammad, Uluğ BayazıtLLM Hallucination Mitigation Privacy-Preserving Language Models
Istanbul Technical University
Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure
Apr 17, 2026Stefan Behfar, Richard MortierCommunication-Efficient Distributed Training Group Fairness
Computer Lab, University of Cambridge, Cambridge, United Kingdom
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.
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Apr 17, 2026Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1Hybrid Quantum-Classical ML High-Energy Physics
qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE
Optimizing Stochastic Gradient Push under Broadcast Communications
Apr 16, 2026Tuan Nguyen, Ting HeCommunication-Efficient Distributed Training Federated Learning
Pennsylvania State University State College, PA, USA
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
Apr 16, 2026Francesco Diana, Chuan Xu, André Nusser +1Gradient Inversion Attacks Adversarial Attacks
Université Côte d’Azur, France · Inria, France · CNRS, France +1
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
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
Apr 16, 2026Thanh Linh Nguyen, Nguyen Van Huynh, Quoc-Viet PhamSynthetic Data Generation Potential Games
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, D02 PN40, Ireland · School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, UK
Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
Apr 16, 2026Adrian Edin, Michel Kieffer, Mikael Johansson +1Communication-Efficient Distributed Training Gradient Compression
Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden · CentraleSupélec, CNRS, L2S, Université Paris-Saclay, Gif-sur-Yvette, FR-91192, France · Division of Decision and Control Systems, School of EECS, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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
HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
Mar 16, 2026Qiyuan Chen, Xian Wu, Yi Wang +1Communication-Efficient Distributed Training Zeroth-Order Optimization
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
Mar 15, 2026Haifeng Wen, Osvaldo Simeone, Hong XingGroup-Conditional Conformal Prediction Conformal Prediction
1IoT Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK · Department of ECE, The Hong Kong University of Science and Technology, HK SAR
Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
Mar 12, 2026Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +2Byzantine-Robust Federated Learning Edge-Cloud Computing
atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Mar 6, 2026Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1Stochastic Optimization Federated Learning
School of Electrical and Computer Engineering, Purdue University
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.
PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems
Feb 21, 2026Nnaemeka Obiefuna, Samuel Oyeneye, Similoluwa Odunaiya +2Privacy-Preserving ML Differential Privacy
1ML Collective · 2Friedrich-Alexander-Universität Erlangen-Nürnberg · 3Carnegie Mellon University
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Feb 20, 2026Ehsan Lari, Reza Arablouei, Stefan WernerFederated Learning Aggregation Byzantine-Robust Federated Learning
Department of Electronic Systems, Norwegian University of Science and Technology, 7491 Trondheim, Norway · Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia · Department of Information and Communications Engineering, Aalto University, 00076, Finland
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
FedPS: Federated Preprocessing for structured data via aggregated Statistics
Feb 11, 2026Xuefeng Xu, Graham CormodeFederated Learning
Department of Computer Science University of Warwick · Department of Computer Science University of Oxford
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

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Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

Yuhan Hu, Xiaolei Fang

Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

RUL Estimation Predictive Maintenance Federated Learning Heterogeneous Federated Learning

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Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.

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