Mitigating Cross-client GANs-based Attack in Federated Learning

Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is usually distributed and privacy sensitive. Multiple distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model without requiring to share their private samples with any third-party entities. In this paper, we show that FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack, in which a malicious client (i.e., adversary) can reconstruct samples with the same distribution as the training samples from other clients (i.e., victims). Since a benign client's data can be leaked to the adversary, this attack brings the risk of local data leakage for clients in many security-critical FL applications. Thus, we propose Fed-EDKD (i.e., Federated Ensemble Data-free Knowledge Distillation) technique to improve the current popular FL schemes to resist C-GANs attack. In Fed-EDKD, each client submits a local model to the server for obtaining an ensemble global model. Then, to avoid model expansion, Fed-EDKD adopts data-free knowledge distillation techniques to transfer knowledge from the ensemble global model to a compressed model. By this way, Fed-EDKD reduces the adversary's control capability over the global model, so Fed-EDKD can effectively mitigate C-GANs attack. Finally, the experimental results demonstrate that Fed-EDKD significantly mitigates C-GANs attack while only incurring a slight accuracy degradation of FL

The largest virialized dark halo in the universe

Using semi-analytic approach, we present an estimate of the properties of the largest virialized dark halos in the present universe for three different scenarios of structure formation: SCDM, LCDM and OCDM models. The resulting virial mass and temperature increase from the lowest values of $1.6 \times 10^{15}h^{-1}M_{\odot}$ and 9.8 keV in OCDM, the mid-range values of $9.0 \times 10^{15}h^{-1}M_{\odot}$ and 31 keV in LCDM, to the highest values of $20.9 \times 10^{15}h^{-1}M_{\odot}$, 65 keV in SCDM. As compared with the largest virialized object seen in the universe, the richest clusters of galaxies, we can safely rule out the OCDM model. In addition, the SCDM model is very unlikely because of the unreasonably high virial mass and temperature. Our computation favors the prevailing LCDM model in which superclusters may be marginally regarded as dynamically-virialized systems.Comment: 5 pages, Accepted by Int. J. Mod. Phys.

Twist-3 distribution amplitudes of the pion and kaon from the QCD sum rules

Twist-3 distribution amplitudes of the pion and kaon are studied in this paper. We calculate the fist several moments for the twist-3 distribution amplitudes (\phi_{p,\sigma}^\pi and \phi_{p,\sigma}^K) of the pion and kaon by applying the QCD sum rules. Our results show that, (i) the first three moments of \phi_p^K and the first two moments of \phi_p^\pi and \phi_\sigma^{\pi,K} of the pion and kaon can be obtained with 30% uncertainty; (ii) the fourth moment of the \phi_p^\pi and the second moment of the \phi_\sigma^K can be obtained when the uncertainty are relaxed to 35%; (iii) the fourth moment of the \phi_\sigma^\pi can be obtained only when the uncertainty are relaxed to 40%; (iv) we have m_{0\pi}^p=1.10\pm 0.08 {\rm GeV} and m_{0K}^p=1.25\pm 0.15 {\rm GeV} after including the \alpha_s-corrections to the perturbative part. These moments will be helpful for constructing the twist-3 wave functions of the pion and kaon.Comment: 18 pages, 10 figures, one more subsection adde