Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Federated singular value decomposition (SVD) is a foundation for many real-world
distributed applications. Existing federated SVD studies either require external servers …

Cross-silo privacy-preserving machine learning (PPML) adopt; Partial Homomorphic
Encryption (PHE) for secure data combination and high-quality model training across …

Cross-silo federated learning (FL) leverages homomorphic encryption (HE) to obscure the
model updates from the clients. However, HE poses the challenges of complex …

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a
promising paradigm that enables the deployment of advanced AI models at the network …

Federated tree-based models are popular in many real-world applications owing to their
high accuracy and good interpretability. However, the classical synchronous method causes …