Latest Federated Learning Research Papers

The newest Federated Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Federated Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Communication-efficient federated learning for edge IoT via combined quantization and sparsification
  Seema Malik, Abhinav Grag, Nishtha Kansal · Discover Electronics · Sep 11, 2026

  Abstract Edge IoT devices are increasingly targeted for on-device intelligence using federated learning (FL). However, conventional FL imposes heavy communication and energy costs that make it impractical for battery-constrained, bandwidth-…

• Secure Federated Learning Based on the OPTICS Clustering Algorithm and Client Selection
  Chunmei Ma, Zhengyang Zhang, Baogui Huang, Chuanwen Luo et al. · Tsinghua Science & Technology · Sep 11, 2026

  Abstract In Federated Learning (FL), the aggregation servers inevitably face attacks from malicious clients because they cannot directly constrain the behaviour of clients. Effectively detecting and mitigating the impact of these malicious …

• Differentially private federated learning for image analysis in smart-city surveillance
  Namrata Pandya, Dhrupa Mistry · Frontiers in Imaging · Sep 10, 2026

  Federated learning combined with differential privacy offers a practical way to train image-analysis models on distributed camera and sensor networks. This combination is most often implemented through DP-SGD, and it avoids centralizing raw…

• Energy-Aware Clustering and Sleep Scheduling with Federated Learning for Faulty Node Detection in Wireless Sensor Networks
  Rachamadugu Subramanay Buvan, V Akhilesh, K. Mallikharjuna Rao · National Academy Science Le... · Sep 10, 2026
• Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks
  Xiao Guo, Xuming He, Xiangyu Chang, Shujie Ma · Journal of the American Sta... · Sep 10, 2026

  Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves com…

• Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence
  K. Ramalakshmi, S. Prema, M. Ajithaveni, M Ramya · IJARCCE · Sep 10, 2026

  The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language proce…

• The Efficiency-Decentralization-Security Trilemma: A Co-Design Framework for Lightweight, Decentralized AI in Cyber-Physical Systems
  Montaser N.A. Ramadan, Hasan Saygın · AI · Sep 10, 2026

  Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two…

• HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
  Osama Abu Hamdan, Rabin Pandey, Hao Che, Engin Arslan et al. · arXiv · Sep 9, 2026

  Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round compl…

• OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
  Ayush Debnath, Ruelia Saha, Sudip Misra · arXiv · Sep 9, 2026

  Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can co…

• Friend-Safe Adversarial Attack for selective evasion in personalized federated learning
  Hyun Kwon, Dae-Jin Kim · Discover Computing · Sep 9, 2026

  Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy; however, the personalization of client models in non-independent and identically distributed (non-IID) settings creates a…

• FedLLMsHydraNet: federated LLMs-driven model to enhance privacy-preserving and decision-making in financial credit risk prediction
  Robin Chauhan, Aditya Bhardwaj, Ajay Kumar, Surendra Kumar · Wireless Networks · Sep 9, 2026
• Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
  Tommaso Cai, Abhinav Chakraborty, Lasse Vuursteen · Journal of the American Sta... · Sep 8, 2026

  This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompa…

• Toward ISAC-Inspired WiFi–Radar Fusion for Privacy-Preserving Elderly In-Home Monitoring
  Xinda Li · Applied and Computational E... · Sep 8, 2026

  Population aging is increasing demand for unobtrusive in-home monitoring, while cameras raise privacy concerns, and wearables depend on user compliance. This critical survey and design study compares WiFi channel-state information sensing a…

• History-Aware Multi-Objective Client Selection for Federated Learning under Statistical and System Heterogeneity
  Haoxuan Geng · Applied and Computational E... · Sep 8, 2026

  Federated learning reduces raw-data movement, but partial participation under statistical and system heterogeneity makes the selected client cohort a major source of optimisation bias and delay. This study proposes a history-aware multi-obj…

• Privacy-Preserving Federated Learning for Crimes Against Humanity in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Pamela Adong Laker · Zenodo (CERN European Organ... · Sep 7, 2026

  The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exc…

• Privacy-Preserving Federated Learning for Creative Clusters Districts in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achilles Byaruhanga Mwesigwa, Doreen Atuhaire Twinomujuni, Muzamiru Ssebuufu Kiggundu · Zenodo (CERN European Organ... · Sep 7, 2026

  Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative distri…

• Privacy-Preserving Federated Learning for Creative Clusters Districts in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achilles Byaruhanga Mwesigwa, Doreen Atuhaire Twinomujuni, Muzamiru Ssebuufu Kiggundu · Zenodo (CERN European Organ... · Sep 7, 2026

  Federated learning offers a promising paradigm for training machine learning models across distributed creative industry clusters without centralising sensitive cultural or commercial data. However, its deployment in Ugandan creative distri…

• Construction of a federated learning-based privacy-preserving and intelligent recommendation model for educational data
  Xi Zhang, Baiying Yang, Chuan Li, Yong Wang · Scientific Reports · Sep 7, 2026
• Privacy-Preserving Federated Learning for Crimes Against Humanity in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Pamela Adong Laker · Zenodo (CERN European Organ... · Sep 7, 2026

  The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exc…

• Consistency alignment with adaptive aggregation to mitigate model drift in federated learning for the Internet of Medical Things
  Xianqiu Meng, Gaochao Xu, Ziying Zhao, Xu Xu et al. · Information Processing & Ma... · Sep 6, 2026
• HE-CloudML: a privacy-preserving framework for secure machine learning inference over encrypted cloud data using homomorphic encryption
  Abdullahi Ahmed Abdirahman, Abdirahman Osman Hashi, Ubaid Mohamed Dahir, Mohamed Abdirahman Elmi · Scientific Reports · Sep 6, 2026

  Abstract The widespread adoption of cloud-based Machine Learning as a Service (MLaaS) exposes sensitive user data to critical privacy risks during inference, as plaintext data must typically be processed by untrusted cloud servers. This pap…

• Privacy-Preserving Federated Learning for Conflict Resolution Peacebuilding in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achieng Mary Goretti Odongo, Nassiwa Brenda Tumusiime, Mugisha Brian Byamukama · Zenodo (CERN European Organ... · Sep 6, 2026

  Federated learning offers a compelling architecture for peacebuilding analytics in conflict-affected regions, where data sovereignty, institutional sensitivity, and infrastructural fragility preclude centralised data aggregation. We introdu…

• Privacy-Preserving Federated Learning for Biophysics Core Life in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Harriet Nansubuga Mutebi, Pamela Atim Okeny, Arthur Tumwijukye Mwesigye · Zenodo (CERN European Organ... · Sep 5, 2026

  Laboratories providing biophysics core services in Uganda hold fluorescence images, electrophysiological traces and macromolecular characterisation records that cannot be pooled in a single server for legal, ethical and infrastructural reas…

• Secure and scalable client pre-filtering and authentication framework for federated learning systems
  Prince Rana, Amritpal Singh · Scientific Reports · Sep 5, 2026

  Abstract A federated learning system, which emphasises decentralisation, allows cooperative model training to ensure the privacy and security of the data. Client authentication is an important factor in a decentralised system to prevent una…

• Privacy-Preserving Federated Learning for Digital Libraries Quarterly Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achieng Atim Irene, Byaruhanga Twinomujuni Joseph, Kemigisa Namutebi Rose · Zenodo (CERN European Organ... · Sep 5, 2026

  Federated learning offers a promising architecture for digital library consortia in low-resource settings, where centralising user interaction data is often infeasible due to privacy concerns and bandwidth constraints. We introduce a client…

• GLA: Structural Regularization via Genetic Optimization of Subspaces for Federated Learning in NLP
  samir · Zenodo (CERN European Organ... · Sep 5, 2026
• Privacy-Preserving Federated Learning for Digital Libraries Quarterly Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achieng Atim Irene, Byaruhanga Twinomujuni Joseph, Kemigisa Namutebi Rose · Zenodo (CERN European Organ... · Sep 5, 2026

  Federated learning offers a promising architecture for digital library consortia in low-resource settings, where centralising user interaction data is often infeasible due to privacy concerns and bandwidth constraints. We introduce a client…

• Privacy-Preserving Federated Learning for Health Informatics Clinical Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels
  Achilles Byaruhanga Kiggwe, Justine Namutebi Nalwoga, Sylvia Achola Olinga · Zenodo (CERN European Organ... · Sep 5, 2026

  Federated learning offers a promising architecture for training clinical machine learning models across distributed Ugandan health facilities without centralising sensitive patient data. However, the practical viability of this approach dep…

• RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
  Quoc H. Nguyen, Ali Lafzi, Abhijeet Phatak, Siddharth Pratap Singh et al. · arXiv · Sep 4, 2026

  Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data heterogeneity that challenges both privacy-preserving training and model personalization. …

• FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification
  Maryam Moradpour, Anne-Christin Hauschild · arXiv · Sep 4, 2026

  Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed across hospitals and cannot be centralized to protect patient privacy. Federated Learning…