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.
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- FedSTD: Federated Learning with Spatio-Temporal Decoupling for Cross-Modal Transport PredictionFabrice Marggi, Eric Samikwa, Torsten Ingo Braun · Open Access CRIS of the Uni... · Dec 15, 2026
Modern transportation systems are shaped by multiple interdependent modes of transport. Understanding their interactions is essential for effective management and planning. Cross-modal flow and demand predictions can improve forecasting acc…
- Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for PortuguesePedro Carvalho Brom · arXiv · Oct 29, 2026
Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…
- DRL-Based Cost-Driven Adaptive Logic Tree Generation for Serverless Federated Learning AggregationHaotian Yan, Xiulin Li, Li Pan, Shijun Liu · Big Data Mining and Analytics · Oct 28, 2026
- Leveraging biased FedSDS as a federated learning strategy for research on non-IID multi-disorder studiesFrancesco Casadei, Patricia A. Apellániz, Luciana Carota, Gianluca Asti et al. · Scientific Reports · Oct 28, 2026
Abstract Federated learning is a powerful approach to overcome privacy and data sharing issues, especially in small sample healthcare settings. However, parameter aggregation strategies, such as FedAvg, are prone to fail in case of non-IID …
- A review of private sample alignment techniques in federated learningQian Zhou, Qing Ye, Yi Yang, Qi Feng et al. · Computer Science Review · Oct 28, 2026
- Physics-Informed Global Model Development via Federated Learning for Indoor Temperature Prediction Toward Advanced Building ControlAbu Talib, Kwangwon Choi, Jaewan Joe · Architectural research · Oct 28, 2026
Model-based predictive control provides an effective approach for improving building energy efficiency by optimizing heating, ventilation, and air-conditioning operation while maintaining indoor thermal conditions. However, its performance …
- Federated Learning for Building Energy Prediction and Fault Detection Across Climate Zones: A Proof-of-Concept StudyJohn Adejoh, Chibuzor Ezeamasiobi, Ugonna Obi-Emeruwa · Cureus Journal of Computer ... · Oct 28, 2026
- Pre-registration: replication of coverage-aware chain ordering (FedDAC-Order) on CIFAR-100 with ResNet-18 and FEMNISTAlasady Ali Salah, Norziana Jamil, Zuhaira Muhammad Zain, Ezedin Baraka et al. · Zenodo (CERN European Organ... · Oct 28, 2026
Pre-registration of a replication experiment for the study 'Grouping or Ordering? Coverage-Aware Sequential Federated Learning under Label Skew'. The plan (REPLICATION_PLAN.md), the analysis script and the job grids were fixed at commit eab…
- Federated and explainable AI for accurate medical diagnosis of Alzheimer’s disease multimodal classificationUppin Rashmi, Beena B M · Multimedia Tools and Applic... · Oct 28, 2026
- FedCD: A Fairness-aware Federated Cognitive Diagnosis FrameworkShangshang Yang, Jialin Han, Xiaoshan Yu, Ziwen Wang et al. · ACM Transactions on Informa... · Oct 27, 2026
With the rapid development of Internet technologies, online intelligent education platforms have generated a vast amount of distributed student learning data. This influx of data presents opportunities for cognitive diagnosis (CD) to assess…
- Tiny Intelligence at the Edge: A Unified TinyML, Federated Learning and Hybrid Geospatial Architecture for Energy-Aware IoT Disaster Monitoringsakshi shinde sakshi · Zenodo (CERN European Organ... · Oct 27, 2026
Disasters such as floods, earthquakes and wildfires are becoming more frequent, yet the sensingnetworks that could warn of them are built from battery-powered microcontrollers that cannot run conventionalmachine learning. This paper present…
- Information-Theoretic Secure Aggregation With Heterogeneous Client QuantizationShreya Meel, Sennur Ulukus, Itzhak Tamo · arXiv · Oct 26, 2026
In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients, without revealing any information on the individual model updates to the server, beyond their sum. Different clients m…
- SCAFFOLD under Hessian Similarity: Sharp Convergence RatesZhirayr Tovmasyan, Slavomír Hanzely, Egor Shulgin, Peter Richtárik et al. · arXiv · Oct 26, 2026
One of the main challenges of federated learning is heterogeneous data split among the clients, leading to biased local updates called client drift. The SCAFFOLD algorithm (Karimireddy et al., 2020) corrects client drift with control variat…
- GenFedEdgeAgri: Diffusion-Enhanced Federated Edge AIoT for Climate-Resilient Precision AgricultureK. Radha Achary, Ajit Kumar Samal · Next-Generation Computing S... · Oct 26, 2026
Modern precision agriculture is undermined by data scarcity, privacy concerns, and high latency in remote areas, and centralized AI models struggle to forecast rare, climate-induced crop stresses given insufficiently diverse datasets and st…
- Enhancing network performance in IoT-WSNs through federated deep reinforcement learning-based intelligent routingN. Lakshmi Kalyani, M. Shailaja, Sravanam Sreeja, Koneti Varalakshmi et al. · International Journal of Re... · Oct 25, 2026
By making it possible to collect and monitor data in real time, the installation of wireless sensor networks (WSNs) in the era of the internet of things (IoT) has brought about a transformation in a number of different industries. On the ot…
- A Federated Edge Artificial intelligence (Edge-AI) Framework For Livestock Health MonitoringDevinder Kaur · Discover Artificial Intelli... · Oct 25, 2026
Precision livestock farming increasingly relies on intelligent monitoring systems to improve animal health, welfare, and farm productivity. However, conventional cloud-centric approaches often face challenges related to data privacy, commun…
- DAFML: A Difficulty-Aware Federated Meta-Learning Algorithm for MEC Offloading and Resource AllocationMan Yang, Zepeng Zhang, Ming Zhang · Information · Oct 25, 2026
In multi-scenario Mobile Edge Computing (MEC), offloading and resource allocation for topologically heterogeneous Directed Acyclic Graph (DAG) tasks are NP-hard, while Federated Meta-Learning (FML) suffers from generalization degradation un…
- Federated Explainable AI-Based Alzheimer's Disease Prediction With Multimodal DataSaniya Aslam Patel · International Journal of Ad... · Oct 25, 2026
Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose early diagnosis benefits from combining several kinds of patient data-brain imaging, cognitive tests, clinical records, and genetic markers. However, these data are …
- Trustaware-x: integrating game-theoretic incentives, reinforcement learning, and shapley value fair compensation in blockchain-secured federated learningPhuc Tran-Vinh, Thai-Minh Truong, Cuong Pham‐Quoc · Journal of Combinatorial Op... · Oct 25, 2026
- A Federated Hybrid Deep Learning Framework for Multimodal MRI and CT Brain Tumor Diagnosis with Explainable AIAmjad Albashayreh · Zenodo (CERN European Organ... · Oct 25, 2026
- Quantum-inspired context-aware adaptive federated edge intelligence for privacy-preserving healthcare analyticsG. Sathish Kumar, Sumendra Yogarayan, Subarmaniam Kannan, M. Karthiga · Discover Artificial Intelli... · Oct 25, 2026
Privacy-preserving healthcare analytics at the edge requires collaborative learning across distributed Internet of Medical Things (IoMT) devices while maintaining strict privacy guarantees, low inference latency, and high predictive accurac…
- Next Generation Intelligent Transportation through Disaster Aware Privacy Preserving Federated LearningT. Srinivasa Rao, D. Venkatarami Reddy, R. Erigela, B. Swetha et al. · International Journal of En... · Oct 25, 2026
The growing challenges posed by rapid urbanization and increased vehicle density have highlighted the need for precise and privacy-preserving traffic flow prediction models. Traditional centralized learning methods involve aggregating priva…
- FEDERATED LEARNING WITH GENERATIVE AI FOR PRIVACY-PRESERVING MEDICAL DATA ANALYSIS : A Comparative Review of Recent Approaches and Open GapsShreya Satyapal Bansode, Prof. Neelam Jain · Zenodo (CERN European Organ... · Oct 24, 2026
Hospitals possess the necessary data for training accurate diagnostic models, but privacy regulations prevent this from happening. Combining the approach of federated learning (FL) with generative models allows for expanding and balancing t…
- Differentially private federated crop-yield benchmarking across sovereign agricultural registries: a privacy-utility frontier analysis for climate analytics competition policyChiamaka Nwakaego Okonkwo-Eze, Ibrahim Dansuleiman Bello, Terseer Mvendaga Iorpuu · Zenodo (CERN European Organ... · Oct 24, 2026
Sovereign agricultural registries hold the crop-yield records that climate-analytics competition policy needs, yet the same records are personal data whose release is constrained by national privacy law. The framework combines a federated L…
- Knowledge-distilled decentralized federated learning for privacy-preserving medical image analysisShiraz Ali Wagan, Mitra Ghergherehchi, Hyojeong Choi, Muhammad Junaid · Ain Shams Engineering Journal · Oct 24, 2026
Federated learning (FL) has proved to be a potential way for decentralized medical institutions to collaborate on model training while preserving data privacy. However, traditional FL approaches rely on centralized aggregation and frequent …
- FABLE: federated adaptive belief learning for cloud-edge semantic cachingYiming Zhang, Donglei Tao, Yunbi Wu, Shigui Li et al. · Journal on Wireless Communi... · Oct 24, 2026
Cloud-edge semantic caching must make proactive decisions from incomplete and evolving regional demand observations. We propose FABLE, a Federated Adaptive Belief Learning framework that maintains a posterior belief over latent demand and u…
- Federated learning for climate-model downscaling across data-scarce national meteorological services: a differential-privacy evaluation of forecast skill and sovereign data leakageChiamaka Nwakaego Okonkwo-Eze, Ibrahim Dansuleiman Bello, Terseer Mvendaga Iorpuu · Zenodo (CERN European Organ... · Oct 24, 2026
National meteorological services in data-scarce regions face a structural tension: statistical downscaling of coarse climate-model output requires large, pooled training corpora, yet the station records that would supply such corpora are so…
- Federated learning of crop-disease classifiers across quarantine-silenced phytosanitary archives: a computer-science evaluation of privacy-utility trade-offs for cross-border outbreak analyticsNourhan Khaled Mostafa · Zenodo (CERN European Organ... · Oct 24, 2026
Phytosanitary archives hold the labelled crop-disease observations that cross-border outbreak analytics require, yet quarantine regulation, commercial confidentiality and national data sovereignty keep those archives closed to centralised t…
- Privacy-Preserving Machine Learning for Mental Health Risk PredictionShahi Sonal, Yadav Pradeep · International Journal of Ph... · Oct 24, 2026
Machine learning can support earlier identification of mental-health risk from electronic health records, speech, social-media text, wearable sensors, and other digital traces, but centralized data collection creates substantial privacy and…
- When Human Corrections Travel: Safety in Federated VLA AdaptationVigneshwaran Subbaraju, Obaidullah Zaland, Monowar Bhuyan · arXiv · Oct 23, 2026
Vision-language-action (VLA) policies can adapt to local human intervention, while federated learning can combine distributed robot experience without pooling raw records. We examine their prospective intersection: sharing adaptations deriv…