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1,772 papers
Biological foundation models are new artificial intelligence tools that can learn useful patterns from large biological datasets and apply them to different research questions to help researchers understand disease mechanisms, identify important genetic changes, predict drug responses, and discover treatment targets.
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...
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This study presents a reproducible proof of concept and a practical baseline for a secure machine learning approach for healthcare collaborations and demonstrates a reproducible proof of concept and a practical baseline for a secure machine learning approach for healthcare collaborations.
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This paper presents a novel approach to differential privacy in federated learning, termed Dynamic Differential Privacy (DDP). The core idea is to adapt the privacy budget dynamically, responding to the varying sensitivity of local data. Traditional federated learning frequently utilizes a static privacy budget, which...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL methods are vulnerable to information leakage and model divergence, particularly when dealing with highly heterogeneous client datasets. This researc...
Findings demonstrate that the proposed framework provides an accurate, privacy-aware, and interpretable solution for decentralized CKD prediction, and maintains robust performance under Gaussian noise.
"This dataset contains the source code and evaluation logs supporting the manuscript \"FedWave: Stealthy Wavelet-Domain Backdoor Attacks in Federated Learning,\" submitted to the IEEE Transactions on Information Forensics and Security.FedWave is an anchor-aligned wavelet-subband backdoor framework for federated learnin...
A conceptual framework is proposed to illustrate the integration of customer behavioral characteristics, policy attributes, and advanced machine learning models into an intelligent decision-support system for claim prediction, highlighting the importance of explainable AI, continuous model monitoring, and human-in-the-...
This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual data...
This paper proposes a novel approach to decentralized federated learning (DFL) that incorporates Byzantine fault tolerance (BFT) using blockchain consensus. Traditional DFL systems are vulnerable to malicious clients injecting poisoned model updates, compromising the overall model accuracy and data integrity. Our syste...
Blockchain consensus still forces a hard choice among scalability, security, and energy use. Redbelly, a leaderless Byzantine Fault Tolerance protocol, largely settles the scalability-versus-energy side of that tension, yet it accepts any cryptographically valid transaction without judging whether it is economically fr...
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.
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.
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.
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.
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.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
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