federated learning

RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints

RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.

AntiBMPNN: Structure‐Guided Graph Neural Networks for Precision Antibody Engineering

AntiBMPNN consistently outperforms AbMPNN, AntiFold, and ProteinMPNN in designing complementarity determining regions (CDR) 1‐3, yielding stronger binding affinities and confirming its potential to improve therapeutic antibody design.

AI-driven PROTAC design overcomes oncogenic resilience by eliminating the CLIP1-LTK fusion protein.

The AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein is reported, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.

Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning

ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.

BBBP-Atlas: Unified Interpretable Modeling of Blood–Brain Barrier Permeability across Small Molecules and Peptides

BBBP-Atlas is proposed, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP, which offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug d...

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow

An integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002 is introduced.

Information-Theoretic Framework for Trustworthy Federated Learning

Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an informat...

A Data Management Mechanism for Federated Learning Models using a CID-based Reference and Verification Structure

Privacy-Enhancing Federated Learning Models for Cybersecurity in IoT Networks

Federated Learning is investigated as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead.

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain-Based Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately com...

A machine learning framework for privacy preserving personalized multimodal emotion recognition

This paper presents a novel machine learning framework, Privacy-Preserving Personalized Multimodal Emotion Recognition (P3MER), that simultaneously addresses three fundamental challenges in affective computing: achieving state-of-the-art recognition accuracy, ensuring robust privacy protection, and enabling effective p...

The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier

Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, McMahan's 2017 FedAvg, and Bonawitz's 2017 aggregation to Zhao...