A privacy-aware federated learning framework for collaborative healthcare IoT security
Rihab Saidi, T. Moulahi, Salah Zidi et al.
· Machine Learning with Applic... · 0 citations
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AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
federated learning
1,863 papers
ZTE-FL: Dual-layer trust aggregation for secure federated learning in IIoT
Abin Kumbalapalliyil Tom, Md. Whaiduzzaman, Ammar Alazab et al.
· Internet of Things · 0 citations
#federated learning
Sep 2026
A federated class-incremental learning framework with dynamic client participation and evolution for machine fault diagnosis
Yaoxiang Yu, Xueyi Li, Guangyao Zhang et al.
· Advanced Engineering Informa... · 2 citations
#federated learning
Sep 2026
Fed-LPAR: Hierarchical personalization and adaptive regularization for differentially private federated learning
Hui Wang, X. Liang, Peiqian Liu et al.
· Knowledge-Based Systems · 0 citations
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#federated learning
Oct 2026
Addressing class imbalance in federated learning: A perspective of catastrophic forgetting
Xiaohan Yuan, Yaheng Wang, Jinlu Xiong et al.
· Chaos, Solitons & Fracta... · 0 citations
Attack-agnostic robust decentralized federated learning
Jiafei Wu, Puning Zhao, Hao Guo et al.
· Knowledge-Based Systems · 0 citations
#federated learning
Oct 2026
Shuffle-model-based federated learning for communication cost–privacy–utility trade-offs
Chunyong Yin, Chenglong Qi
· Computer Networks · 0 citations
#federated learning
Oct 2026
Toward a Federated Cross-Organizational Construction Safety Knowledge Recommendation: A Dual-Incentive Mechanism Integrating Worker Satisfaction
This study proposes a dual-incentive mechanism that integrates model training performance with worker satisfaction, encouraging providers to allocate more resources to safety knowledge recommendation and shows that the dual-incentive mechanism yields stronger incentive intensity, higher provider effort, and greater req...
Shuai Zhang, Botao Zhong, Samad M. E. Sepasgozar et al.
· Journal of construction engi... · 0 citations
#federated learning
Oct 2026
Dual prototypes for heterogeneous Federated Learning: A degradation perspective
Wen Ou, Tianchi Liao, Chuan Chen
· Engineering applications of... · 0 citations
Secure and efficient Federated Learning using sketches and Fully Homomorphic Encryption
E. M. Sarmento, J. Batista, J. J. Bastos et al.
· Journal of Network and Compu... · 0 citations
#federated learning
Oct 2026
Data-distribution-driven clustered federated learning with dual-perturbation
B.A. Jiang, Shengnan Zhao, Jiyang Chen et al.
· Knowledge-Based Systems · 1 citation
#federated learning
Oct 2026
Federated few-shot learning with explainable prototype representations for tuberculosis detection in chest X-rays
Safa Ben Atitallah, Maha Driss, Wadii Boulila et al.
· Information Sciences · 0 citations
From tech blogs
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MIT News · Artificial Intelligence
· news.mit.edu
Oct 14, 2026
Researchers improve weather forecasts looking two to six weeks out
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Google DeepMind Blog
· deepmind.google
Oct 14, 2026
Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence
· news.mit.edu
Oct 13, 2026
How artificial neural networks build their own learning machinery
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Microsoft Research Blog
· microsoft.com
Oct 12, 2026
Simulating student learning for better AI tutoring
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence
· news.mit.edu
Oct 7, 2026
Discovering the value of humanistic inquiry
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Microsoft Research Blog
· microsoft.com
Oct 7, 2026
Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.