Our findings reveal that even small collectives (controlling less than 0.01% of the training data) can achieve up to

Discriminative methods were used to execute such tasks and achieved state-of-the-art performance.

Despite efficiencyimprovements, existing methods overlook themisguidance in anchors learning induced by partial missing samples,i.e., the absence of samples results in shift of learned anchors, further leading to sub-optimal clustering performance.

In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk.

Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.

Recent high-performing object detectors, such asFaster R-CNN [34],formulate thedetection problem as atwo-stage pipeline.