Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

It has been widely observed that deep neural networks (DNN) are vulnerable to backdoor
attacks where attackers could manipulate the model behavior maliciously by tampering with …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …

Pre-trained vision models (PVMs) have become a dominant component due to their
exceptional performance when fine-tuned for downstream tasks. However, the presence of …

Federated learning (FL) facilitates distributed training across different IoT and edge devices,
safeguarding the privacy of their data. The inherent distributed structure of FL introduces …

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 …

Backdoor attacks pose a significant threat to the security of federated learning systems.
However, existing research primarily focuses on backdoor attacks and defenses within the …

As machine learning (ML) usage becomes more popular in the healthcare sector, there are
also increasing concerns about potential biases and risks such as privacy. One …

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative
deep learning model training across distributed data silos. Despite its importance, FL faces …