The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Abstract Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that
focuses on recognizing facial expressions in video format. Previous research has …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

Empirical defenses for machine learning privacy forgo the provable guarantees of
differential privacy in the hope of achieving higher utility while resisting realistic adversaries …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Large language models are rapidly gaining popularity and have been widely adopted in real-
world applications. While the quality of training data is essential, privacy concerns arise …

Federated learning enables multi-participant joint modeling with distributed and localized
training, thus effectively overcoming the problems of data island and privacy protection …

This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

Direct speech-to-speech translation (S2ST) with discrete units leverages recent progress in
speech representation learning. Specifically, a sequence of discrete representations derived …

Abstract In Federated Learning (FL), models are as fragile as centrally trained models
against adversarial examples. However, the adversarial robustness of federated learning …