Differentially Private Federated Learning

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Overview

Differentially private federated learning studies how to train shared models across organizations or devices without exposing sensitive training data, using formal privacy guarantees to limit information revealed by the model or its updates. Common approaches clip and perturb gradients or client updates with calibrated noise, often alongside secure aggregation, privacy accounting, adaptive clipping, client selection, or parameter-efficient tuning such as LoRA. Work spans medical imaging, language models, intrusion detection, and other tasks with non-IID client data, where utility is assessed against privacy budgets and sometimes membership-inference or poisoning attacks. The central tension is that stronger privacy can reduce accuracy—especially for heterogeneous or rare data—while robustness, fairness, communication costs, and the precise scope of each privacy guarantee also need to be addressed together.

Latest papers

CardsList

65 papers5 papers in the last four weeks, down 55% on the four weeks before. 0.0% of all new papers.

  1. DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models

    May 11, 2026Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2Privacy-Preserving Language ModelsDifferentially Private Stochastic Gradient Descent

    Samsung R&D Institute UK (SRUK) · Samsung AI Centre Cambridge

  2. UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment

    May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Multimodal Federated LearningFederated Learning Aggregation

    National Taiwan University · RIKEN-CCS · The University of Osaka +1

  3. Modulated learning for private and distributed regression with just a single sample per client device

    May 8, 2026Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1Differentially Private Federated LearningDifferential Privacy

    Massachusetts Institute of Technology (MIT) · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

  4. Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning

    May 7, 2026Myoungjun Kim, Sangwoo Park, Yoseob Han +1Differentially Private Federated LearningFederated Learning

    Myongji University · King’s College London · Soongsil University

  5. Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

    May 4, 2026Judith Sáinz-Pardo Díaz, Álvaro López GarcíaPrivacy-Preserving MLDifferentially Private Federated Learning

  6. Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning

    Apr 30, 2026Yuhua Wang, Qinnan Zhang, Xiaodong Li +6Privacy-Preserving MLDifferentially Private Federated Learning

    School of Artificial Intelligence, Beihang University · School of Statistics, Renmin University of China · School of Computer Science and Engineering, Beihang University

  7. Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

    Apr 25, 2026Emre Ardıç, Yakup GençNon-IID Federated LearningDifferentially Private Federated Learning

    Department of Computer Engineering, Gebze Technical University, Gebze, Kocaeli 41400, Türkiye

  8. Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

    Apr 22, 2026Jie Xu, Haaris Mehmood, Rogier Van Dalen +2Federated Learning AggregationNon-IID Federated Learning

    Samsung R&D Institute UK (SRUK) · Samsung AI Centre Cambridge

  9. Secure and Privacy-Preserving Vertical Federated Learning

    Apr 15, 2026Shan Jin, Sai Rahul Rachuri, Yizhen Wang +2Privacy-Preserving Federated LearningFederated Learning

    Visa Research

  10. Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

    Apr 14, 2026Gustavo de Carvalho BertoliMembership Inference AttacksDifferentially Private Federated Learning

  11. Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

    Apr 8, 2026Wenjing Wei, Farid Nait-Abdesselam, Alla JamminePrivacy-Preserving Federated LearningDifferentially Private Federated Learning

    Université Paris Cité, Paris, France

  12. ADP-FL-MedSeg: Adaptive Differential Privacy for Federated Medical Segmentation Across Diverse Modalities

    Apr 7, 2026Puja Saha, Eranga UkwattaDifferentially Private Federated LearningMedical Image Segmentation

    College of Engineering, University of Guelph, 50 Stone Road E, Guelph, ON, Canada

  13. Communication-Efficient Distributed Learning with Differential Privacy

    Apr 2, 2026Xiaoxing Ren, Yuwen Ma, Nicola Bastianello +3Communication-Efficient Distributed TrainingDifferentially Private Federated Learning

    School of Civil and Environmental Engineering, Cornell University, Ithaca, New York, United States. · Department of Electronic and Electrical Engineering, University College London, London WC1E 7JE, United Kingdom. · School of Electrical Engineering and Computer Science, and Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden. +4

  14. BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy

    Apr 2, 2026Abhilash Kar, Basisth Saha, Tanmay Sen +1Multimodal Survival AnalysisFederated Learning

    SQC and OR Unit, Indian Statistical Institute, Kolkata, India · Department of Statistics, Presidency University, Kolkata, India

  15. Federated fairness-aware classification under differential privacy

    Mar 25, 2026Gengyu Xue, Yi YuAlgorithmic FairnessDifferentially Private Federated Learning

    Department of Statistics, University of Warwick

  16. Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions

    Mar 24, 2026Rustem Islamov, Grigory Malinovsky, Alexander Gaponov +3Byzantine-Robust Federated LearningDifferentially Private Federated Learning

    University of Basel, Switzerland · KAUST, Saudi Arabia · MBZUAI, UAE

  17. Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication

    Mar 22, 2026Idris Zakariyya, Pai Chet Ng, Kaushik Bhargav Sivangi +3Differentially Private Federated LearningVideo Action Recognition

    School of Computing Science, University of Glasgow, G12 8QQ, Glasgow, UK · Infocomm Technology Cluster, Singapore Institute of Technology, Singapore · The Edward S. Rogers Sr. Dept. of Electrical and Computer Engineering, University of Toronto, Canada

  18. When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence

    Mar 19, 2026Chen Yaoling, Liang Hao, Tu XiaotongDifferentially Private Federated LearningFederated Learning

    School of Informatics, Xiamen University, China · Internet of Things Thrust, Hong Kong University of Science and Technology (Guangzhou), China

  19. Federated Learning for Privacy-Preserving Medical AI

    Mar 16, 2026Tin HoangDifferentially Private Federated LearningDifferential Privacy

    School of Computer Science and Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK

  20. DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning

    Mar 14, 2026Renuga Kanagavelu, Manjil Nepal, Ning Peiyan +6Anti-Money Laundering DetectionDifferentially Private Federated Learning

    A*STAR IHPC Singapore · SRM University-AP Andhra Pradesh, India · EVYD Singapore

  21. Resource-Adaptive Federated Text Generation with Differential Privacy

    Mar 7, 2026Jiayi Wang, John Gounley, Heidi HansonDifferentially Private Federated LearningSynthetic Data Generation for Language Models

    Oak Ridge National Laboratory Oak Ridge, TN, USA

  22. Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques

    Mar 5, 2026Yenan Wang, Carla Fabiana Chiasserini, Elad Michael SchillerFederated LearningDifferentially Private Federated Learning

    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden

  23. Accurate, private, secure, federated U-statistics with higher degree

    Mar 2, 2026Quentin Sinh, Jan RamonU-StatisticsDifferentially Private Federated Learning

    MAGNET · MAGNET, Univ. Lille, INRIA, CNRS, UMR 9189 - CRIStAL, F-59000 Lille, France.

  24. A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning

    Feb 28, 2026Rodrigo Tertulino, Laércio AlencarCardiovascular Risk PredictionDifferentially Private Federated Learning

    Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN), Natal 3030-290, RN, Brazil

  25. Tackling Privacy Heterogeneity in Differentially Private Federated Learning

    Feb 26, 2026Ruichen Xu, Ying-Jun Angela Zhang, Jianwei HuangDifferentially Private Federated LearningClient Selection in Federated Learning

    Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong · School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network, and CSIJRI Joint Research Centre on Smart Energy Storage, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China

  26. JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning

    Feb 25, 2026Ruichen Xu, Ying-Jun Angela Zhang, Jianwei HuangIncentive Mechanisms for Federated LearningClient Selection in Federated Learning

    Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong · School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network, and CSIJRI Joint Research Centre on Smart Energy Storage, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China

  27. DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models

    Feb 23, 2026Jin Liu, Yinbin Miao, Ning Xi +1Differentially Private Federated LearningFederated Learning

    School of Cyber Engineering, Xidian University, Xi’an, China · College of Intelligence and Computing, Tianjin University, Tianjin, China

  28. Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models

    Feb 23, 2026Jin Liu, Yinbin Miao, Ning Xi +1Federated Fine-TuningLow-Rank Adaptation

    School of Cyber Engineering, Xidian University · College of Intelligence and Computing, Tianjin University

  29. PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT

    Feb 20, 2026Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan +3Blockchain-Based Federated LearningIoT Intrusion Detection

    Information Security Lab, University of Information Technology, Ho Chi Minh City, Vietnam. · Vietnam National University, Ho Chi Minh City, Vietnam. · VNU-HCM Information Security Center, Ho Chi Minh City, Vietnam.

  30. Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning

    Feb 19, 2026Obaidullah Zaland, Sajib Mistry, Monowar BhuyanTraining Data Reconstruction AttacksPrivacy-Preserving ML

    Department of Computing Science, Umeå Unviersity, Umeå, SE-90187, Sweden · Curtin University, Bentley WA 6102, Australia