TL;DR
Findings indicate that the integration of stabilisation, directional filtering, and trimmed-mean aggregation improves empirical robustness under the evaluated adversarial settings, although the present study does not establish formal Byzantine fault-tolerance or convergence guarantees.
Federated learning (FL) has gained prominence in decentralized artificial intelligence applications; however, its susceptibility to adversarial attacks, particularly GAN-based poisoning, remains a critical challenge. This study proposes a decentralized, multi-layered security framework integrating blockchain-backed val...
RetFL is proposed, a CKKS-enabled robust aggregation framework for DFL that establishes a decentralized training workflow with VRF-based candidate selection and view change, and designs a weighted aggregation scheme that incorporates cosine similarity and a dynamic reputation mechanism to weight updates and suppress pe...
Federated Learning (FL) enables distributed model training without sharing raw data, making it particularly suitable for Internet of Things (IoT) environments. However, FL remains vulnerable to poisoning attacks, where malicious clients manipulate local updates to corrupt global model optimization. Existing defenses ma...
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their raw data, making it a promising paradigm for privacy-sensitive generative applications. However, diffusion models trained in federated environments remain vulnerable to backdoor, evasion, and model-po...
A theoretical analysis showing that, under bounded retained residuals, the aggregation error is controlled by the candidate-set Byzantine contamination, the magnitude of retained Byzantine residuals, and the stability of attention weights, achieves competitive or superior robustness compared with classical and recent r...
With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged. In simple terms, FL enables multiple entities to collaboratively train AI models without sharing local data. The FL paradigm is particularly valuable in privacy-sensitive...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
