Rongfei Jia

TL;DR: A secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone is designed, which features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with customized plausible deniability against the position of its desired submodel, thereby protecting private data.

TL;DR: A secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone is designed, which features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with a customized plausible deniability against the position of her desired submodel, thus protecting her private data.

TL;DR: Two new distributed optimization algorithms called multi-model parallel SGD (MM-PSGD) and multi-chain parallelSGD (MC-PS GD) are proposed with a convergence rate of $O(1/\sqrt{NT})$, achieving a linear speedup with respect to the total number of clients.

TL;DR: Zhang et al. as discussed by the authors proposed a method called Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in FL without a test dataset, which achieves this using the statistical correlation of the model parameters uploaded by users.

TL;DR: In this article, the authors proposed Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in federated learning without a test dataset, which achieves this using the statistical correlation of the model parameters uploaded by users.