3 accepted papers
We propose Stacked Deep Sets and Quantile Pooling for learning tasks on set data. We introduce Quantile Pooling, a novel permutation-invariant pooling operation that synergizes max and average pooling. Just like max pooling, quantile pooling emphasizes the most salient features of the data. Like ave…
In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their…
In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a rational metric for distributing financial benefits among federated participants, but also helps to discover malicious p…