This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee
using local differential privacy (LDP). Existing LDP protocols are developed primarily to …

Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep
neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivial to …

Federated learning (FL) is an emerging distributed machine learning framework for collaborative
model training with a network of clients (edge devices). FL offers default client privacy …

Federated learning (FL) is an emerging distributed machine learning framework for collaborative
model training with a network of clients (edge devices). FL offers default client privacy …

Real-time object detection is one of the key applications of deep neural networks (DNNs) for
real-world mission-critical systems. While DNN-powered object detection systems celebrate …

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing
nature. Existing defense solution usually requires larger amount of computation in either the …

Non-ribosomal peptide synthetases are important enzymes for the assembly of complex
peptide natural products. Within these multi-modular assembly lines, condensation domains …

Over the last decade, a number of research studies have advocated the use of information
technology (IT) in different aspects of logistics and distribution operations. This study …

Ensemble learning is a methodology that integrates multiple DNN learners for improving
prediction performance of individual learners. Diversity is greater when the errors of the …

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by
integrating external knowledge to reduce hallucinations and incorporate up-to-date information …