Federated learning presents massive potential for privacy-friendly vision task collaboration.
However, the federated visual performance is deeply affected by backdoor attacks, where …

Motivated by high resource costs of centralized machine learning schemes as well as data
privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on …

We investigate federated self-supervised representation learning (FedSSRL) and federated
clustering (FedCl), aiming to derive low-dimensional representations of datasets distributed …

In traditional Federated Learning approaches like FedAvg, the global model underperforms
when faced with data heterogeneity. Personalized Federated Learning (PFL) enables clients …

Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated
by interacting with their environments, far more than what can be stored or transmitted with …

We investigate contrastive learning in the federated setting through the lens of SimCLR and
multi-view mutual information maximization. In doing so, we uncover a connection between …

Recent research in computer vision for intelligent transportation systems has prominently
focused on image-based depth estimation due to its cost-effectiveness and versatile …

Monocular depth estimation is gaining attention in computer vision for autonomous driving
due to its cost-effectiveness and versatility. Recent works have used self-supervised …

Personalized federated learning (PFL) is a popular distributed learning framework that
allows clients to have different models and has many applications where clients' data are in …

Many studies integrate federated learning (FL) with self-supervised learning (SSL) to take
advantage of raw data distributed across edge devices. However, edge devices often …