Privacy-Preserving Framework

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Period ending 2026-09-21

6 new papers

A weekly snapshot of new work published in Privacy-Preserving Framework.

Period ending 2026-09-14

3 new papers

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Period ending 2026-09-07

5 new papers

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139 papers

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From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
Jun 9, 2026Huong Nguyen, Mickaël Bettinelli, Amirhossein Ghaffari +4Federated Learning Heterogeneity
Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
Jun 9, 2026Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange +1Federated Learning Efficient Continual Learning
SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
Jun 6, 2026Steven Golob, Sikha Pentyala, Martine De CockSynthetic Tabular Data Privacy-Preserving Framework
CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
Jun 6, 2026Yongqi Jiang, Yansong Gao, Siguang Chen +1Privacy-Preserving Framework Causal Representation Learning
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
Jun 2, 2026Nishit SinghFairness-Aware Learning Privacy-Preserving Framework
A Locally Deployed RAG-Based Academic Advising System for Course Selection
Jun 2, 2026Feng Li, Yoritaka IwataAcademic Integrity Privacy-Preserving Framework
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Jun 1, 2026Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella SunFederated Learning Differential Privacy
Coherent Swap Regret and Channel-Proof Learning
Jun 1, 2026Sohail SarkarPost-Quantum Cryptography O(T^Β)$ Simultaneous Regret
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
May 31, 2026Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi +1Federated Learning Privacy-Preserving Framework
LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability
May 29, 2026Tom Lucas, Alessio Buscemi, Alfredo Capozucca +2Large Language Model Evaluation Transparency
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
May 29, 2026Jabin Koo, Hoyoung Kim, Minwoo Jang +1Preference Alignment Preference Learning
Primitive Subspaces Mediate Few-Shot Transfer in VLAs
May 29, 2026Anya Singh, Cabrel Happi, Jai Relan +2Vision-Language-Action Models Few-Shot Learning
FPLIER: Federated Pathway-Level Information Extractor
May 28, 2026Daniele Malpetti, Christian Berchtold, Francesco Gualdi +3Privacy-Preserving Framework Spatial Transcriptomics
Does Distributed Training Undermine Compute Governance?
May 28, 2026Robi RahmanDistributed Optimization Privacy-Preserving Framework
Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
May 27, 2026Guillaume Larue, Louis-Adrien Dufrène, Quentin Lampin +2Two-Layer Neural Networks Neural Network
Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
May 27, 2026M. Generali Lince, V. Divol, R. Flamary +2Fairness-Aware Learning Disparities
Pattern Recognition Tasks with Personalized Federated Learning
May 27, 2026Md. Arifur Rahman, Isha Das, Mushfiqur Rahman Abir +4Federated Learning Privacy-Preserving Framework
Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms
May 26, 2026Meghana Bhange, Ulrich Aïvodji, Elliot CreagerFairness-Aware Learning Subgroup Information
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Kernel Method Dimensionality Reduction
Practical Anonymous Two-Party Gradient Boosting Decision Tree
May 26, 2026Chenyu Huang, Fan Zhang, Minxin Du +6Privacy-Preserving Framework Homomorphic Encryption
Separate Aggregation of Split Network for Personalized Federated Learning
May 26, 2026Yunseok Kang, Jaeyoung SongHeterogeneous Federated Learning Federated Learning
Rethinking Federated Unlearning via the Lens of Memorization
May 23, 2026Jiaheng Wei, Yanjun Zhang, He Zhang +5Federated Learning Privacy-Preserving Framework
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Federated Learning Ensemble
PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
May 22, 2026Anisa Halimi, Liubov Nedoshivina, Kieran Fraser +1Federated Learning Privacy-Preserving Framework
Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence
May 22, 2026Yannik Dittmar, Marvin Jerome Stephan, Thomas Völkl +2Privacy-Preserving Framework Device
FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
May 21, 2026Rachid HedjamFederated Learning Privacy-Preserving Framework
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
May 20, 2026Penglin Dai, Fulian Li, Xincao Xu +3Federated Learning Fedavg
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
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
BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
May 17, 2026Yuhan Xie, Chen Lyu, Jingrong HuangFederated Learning Fairness-Aware Learning
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
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
SAFE Quantum Machine Learning with Variational Quantum Classifiers
May 15, 2026Ying Chen, Paolo Giudici, Vasily Kolesnikov +1Variational Quantum Circuits Privacy-Preserving Framework
Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis
May 13, 2026Haebom Lee, Gyeongjung KimPrivacy-Preserving Framework Feature Learning
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
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections
May 13, 2026Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2Privacy-Preserving Framework
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
May 12, 2026Ehsan Lari, Reza Arablouei, Stefan WernerByzantine Attacks Conformal Prediction
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
May 11, 2026Ben Rachmut, Luise Ge, William Yeoh +2Heterogeneous Federated Learning Federated Learning
Concordia: Self-Improving Synthetic Tables for Federated LLMs
May 11, 2026Jimin Huang, Duanyu Feng, Nuo Chen +8Federated Learning Large Language Model Adaptation
CalBench: Evaluating Coordination-Privacy Trade-offs in Multi-Agent LLMs
May 10, 2026Chelsea Zou, Yiheng Yao, Selena She +2Multi-Agent Large Language Model Artificial Intelligence Assistants
Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
May 10, 2026John Cartmell, Alexander WilliamsInternet Of Thing Privacy-Preserving Framework
FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference
May 9, 2026Qijun Hou, Yuchen Shi, Pingyi Fan +1Federated Learning Fedavg
Quotient Semivalues for False-Name-Resistant Data Attribution
May 8, 2026Florian A. D. Burnat, Brittany I. DavidsonShapley Value Fairness-Aware Learning
Decentralized Conformal Novelty Detection via Quantized Model Exchange
May 7, 2026Kyle Loh, Yu XiangFalse Discovery Rate Privacy-Preserving Framework
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
May 7, 2026Su Zhang, Junfeng Guo, Heng HuangFederated Learning Privacy-Preserving Framework
CRISP: Compositional Relations as Invariant Structural Priors for Domain Generalization
May 7, 2026Dat Nguyen, Duc-Duy NguyenMultimodal Domain Generalization Domain Generalization
Enabling Federated Inference via Unsupervised Consensus Embedding
May 7, 2026Yui Hashimoto, Takayuki Nishio, Yuichi Kitagawa +1Federated Learning Privacy-Preserving Framework
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
May 6, 2026Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2Federated Learning Fairness-Aware Learning
FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling
May 6, 2026Guangyi Zhang, Yi Dai, Yiyun He +1Federated Learning Single-Cell Rna Sequencing
FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
May 5, 2026Farjana YesminHealthcare Privacy-Preserving Framework
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
May 4, 2026Chamani Shiranthika, Hadi Hadizadeh, Parvaneh SaeediFederated Learning Medical Image Segmentation
Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
May 4, 2026Judith Sáinz-Pardo Díaz, Álvaro López GarcíaFederated Learning Differential Privacy
Graph Federated Unlearning for Privacy Preservation
May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated Graph Learning Federated Learning
A Target-Free Harmonization Method for MRI
May 2, 2026Minjun Kim, Dong Ju Mun, Hwihun Jeong +4Magnetic Resonance Imaging Reconstruction Magnetic Resonance Imaging
FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization
May 1, 2026Zoe Fowler, Ghassan AlRegibFederated Learning Fedavg
DeRelayL: Sustainable Decentralized Relay Learning
Apr 30, 2026Haihan Duan, Tengfei Ma, Yuyang Qin +4Decentralization Privacy-Preserving Framework
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
Apr 29, 2026Zhaoyuan Cai, Xinglin ZhangUnsupervised Privacy-Preserving Framework
Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization
Apr 28, 2026Teetat Pipattaratonchai, Aueaphum AueawatthanaphisutChemical Process Systems Federated Learning
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
Apr 27, 2026Zihan Liu, Yizhen Wang, Rui Wang +2Large Language Model Fine-Tuning Privacy-Preserving Framework

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

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

Huong Nguyen, Mickaël Bettinelli, Amirhossein Ghaffari, Alexandre Benoit+3

Future Computing Group, University of Oulu, Finland · LISTIC, University Savoie Mont Blanc, France · AaltoSEA Group, Aalto University, Finland

Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vulnerabilities and challenges, and a major determinant of a stable and well-converged training. Existing FL reviews describe general foundations, security practices, opportunities, challenges, and applications, without delving into diverse aspects of data and considering problems from the data perspective. They rarely provide a data-lens synthesis that links concrete data properties, split protocols, and defenses to convergence speed and stability. This survey fills that gap with three advances. First, we analyze non-IID into measurable traits and rank their influence on convergence as strong, medium, or light, explaining the mechanisms behind each and reconciling evidence across images, texts, and graphs. Second, we connect experimental splitting practices to the real phenomena they emulate, expose the artifacts they introduce, and show how those artifacts affect target accuracy. Third, we analyze how data-related vulnerabilities and their proposed defenses affect convergence, reporting performance under clean and adversarial conditions to make the convergence-robustness trade-off explicit. To our knowledge, this is the first survey to provide a complete understanding of data-related challenges that govern FL. With clear takeaways distilled for each concern, our work serves as actionable guidance, helping practitioners design their system with predictable convergence and stability.

Federated Learning Heterogeneity Privacy-Preserving Framework Private Data Convergence Vulnerability Detection & Repair Review

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