Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …

Recent works on neural contextual bandits have achieved compelling performances due to
their ability to leverage the strong representation power of neural networks (NNs) for reward …

Abstract Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL)
research for their architectural advantages. CNN relies heavily on hyperparameter …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

Federated learning (FL) over mobile networks has attracted intensive attention recently.
User mobility is a fundamental feature of mobile networks, which leads to dynamic network …

Research in the field of hyperparameter optimization (HPO) has been greatly accelerated by
existing HPO benchmarks. Nonetheless, existing efforts in benchmarking all focus on HPO …

With the development of Consumer Electronic Products (CEP) in the Computer,
Communication, and Consumer electronics (3C) industry, research over high speed, high …

SecureBoost is a tree-boosting algorithm that leverages homomorphic encryption (HE) to
protect data privacy in vertical federated learning. SecureBoost and its variants have been …

Federated Learning (FL) has empowered advancements in machine learning by using
model sharing as an alternative to data sharing. This feature avoids uploading huge …