Federated Learning

Dist-SI: Selective Inference with Distributed Data via Randomized Lasso.

Dist-SI: Selective Inference with Distributed Data via Randomized Lasso

Leave a Comment / Machine Learning, Math Applications, Medical AI /

Dist-SI: Selective Inference with Distributed Data via Randomized Lasso | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Statistical Inference · Journal of Machine Learning Research 26 (2025) 1–44 · 20 min read How Dist-SI Lets Hospitals Run Joint Studies Without Sharing Patient Records — Selective Inference Across Distributed Data Sifan Liu (Stanford) and […]

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Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback.

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

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Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Federated Learning · Journal of Machine Learning Research 26 (2025) 1–67 · 18 min read The Sampling Problem Federated Learning Has Been Ignoring — and How OSMD Finally Fixes It A multi-institution team

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DisC2o-HD: Distributed Causal Inference with Covariate Shift for High-Dimensional Healthcare Data.

DisC2o-HD: Distributed Causal Inference with Covariate Shift for High-Dimensional Healthcare Data

Leave a Comment / Medical AI, Math Applications /

DisC2o-HD: Distributed Causal Inference with Covariate Shift for High-Dimensional Healthcare Data | AI Trend Blend AITrendBlend Healthcare AI Math Applications About Healthcare AI · Journal of Machine Learning Research 26 (2025) · Penn / Columbia / Cornell · 20 min read DisC2o-HD: How Researchers Are Solving the Privacy-Accuracy Trade-off in Multi-Hospital Causal Inference A team

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FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer.

FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer

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FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About Federated Learning · Expert Systems With Applications 306 (2026) 130937 · 22 min read FedLSC: The Smarter Way to Train a Skin Cancer AI Across Hospitals Without Sharing Any Patient Data Researchers at

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FCUCR: Federated Continual Recommendation That Remembers You Without Storing. Your Data.

FCUCR: Federated Continual Recommendation That Remembers You Without Storing Your Data

Leave a Comment / Machine Learning, Cybersecurity, Recommender Systems /

FCUCR: Federated Continual Recommendation That Remembers You Without Storing Your Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision NLP Recommenders System About Recommender Systems · Federated AI · ACM Web Conference 2026 (WWW ’26) · arXiv:2603.17315 · 16 min read FCUCR: The Recommender System That Learns Who You’re Becoming — Without Ever Seeing

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The FedDRLPD system architecture.

FedDRLPD: Deep Reinforcement Learning Defense Against Poisoning Attacks in Federated Learning

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FedDRLPD: Deep Reinforcement Learning Defense Against Poisoning Attacks in Federated Learning | AI Security Research AISecurity Research Machine Learning About Federated Learning Security · Knowledge-Based Systems 2026 · 16 min read FedDRLPD: Teaching AI to Defend Itself Against Poisoning Attacks Through Deep Reinforcement Learning A novel defense framework that integrates Deep Q-Network algorithms with Mahalanobis

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Skin Cancer Detection Model

Revolutionizing Skin Cancer Detection: How Multimodal AI and Federated Learning Are Transforming Dermatological Diagnostics

1 Comment / Machine Learning, Computer Vision, Cybersecurity /

Introduction: The Critical Need for Intelligent, Privacy-Preserving Skin Cancer Diagnosis Skin cancer remains one of the most pervasive and life-threatening health conditions globally, with over 5 million new cases reported annually in the United States alone. Among the various types, malignant melanoma stands out as particularly alarming—accounting for approximately 4% of global cancer-related deaths and

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A visual comparison of original, reconstructed, and noise-injected medical images under federated learning to illustrate privacy risks and shadow defense impact.

🔒7 Alarming Privacy Risks of Federated Learning—and the Breakthrough Shadow Defense Fix You Need

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Introduction Federated Learning (FL) has been heralded as the privacy-preserving future of AI, especially in sensitive domains like healthcare. But behind its collaborative promise lies a serious vulnerability: gradient inversion attacks (GIA). These attacks can reconstruct original training images from shared gradients—exposing confidential patient data. Enter the breakthrough: Shadow Defense. In this article, we dive

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