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2,117 papers
It is argued that offering federated computing as an important AmSC service would unlock privacy-constrained scientific collaborations, enabling public-private partnerships in model building while exercising and enhancing the platform's own federated infrastructure.
This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...
It is suggested that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, FL systems are vulnerable to Byzantine attacks, where malicious participants introduce corrupted data or models to compromise the learning process. This pap...
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fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud, is introduced, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud that enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel dec...
The Wisdom Continuum: A Unified Theory of Human Development, Learning, Intelligence, Artificial Intelligence, and Civilizational Evolution Description / Abstract The Wisdom Continuum presents a civilizational-scale framework that unifies human development, moral philosophy, educational reform, and artificial intelligen...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL approaches often struggle with data heterogeneity, communication bottlenecks, and, crucially, privacy concerns. This paper proposes a novel dec...
Concept drift can degrade encrypted-traffic classifiers deployed at the network edge as applications, protocols, and usage patterns evolve. This paper formulates federated continual learning under asynchronous real- and virtual drift and proposes DriftGuard, a framework combining a two-level per-node detector, selectiv...
This record provides the version 1.0.0 software and reproducibility package supporting the manuscript Privacy-Aware Guarded Federated Scorebank Ensembles for Unsupervised Edge Insider-Threat Detection. The implementation studies heterogeneous cross-silo collaboration using three CMU CERT release-based clients. It inclu...
Federated learning (FL) lets distributed intrusion-detection systems (IDS) train on private traffic, but exposes the global model to poisoning and backdoor attacks under non-IID client data. A popular defense family scores or filters clients using a small trusted "root" dataset and the similarity of client behaviour to...
These findings demonstrate that the proposed framework provides an effective balance between privacy preservation, adversarial robustness, and trustworthy decentralized collaborative learning for secure AI-driven systems.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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