Morteza Amini

Yasaman Saadati, Mohammad Rostami, M. Hadi Amini

Traditional Federated Learning (FL) methods encounter significant challenges when dealing with heterogeneous data and providing personalized solutions for non-IID scenarios. Personalized Federated Learning (PFL) approaches aim to address these issues by balancing generalization and personalization, often through parameter decoupling or partial models that freeze some neural network layers for personalization while aggregating other layers globally. However, existing methods still face challenges of global-local model discrepancy, client drift, and catastrophic forgetting, which degrade model accuracy. To overcome these limitations, we propose $\textit{pMixFed}$, a dynamic, layer-wise PFL approach that integrates $\textit{mixup}$ between shared global and personalized local models. Our method introduces an adaptive strategy for partitioning between personalized and shared layers, a gradual transition of personalization degree to enhance local client adaptation, improved generalization across clients, and a novel aggregation mechanism to mitigate catastrophic forgetting. Extensive experiments demonstrate that pMixFed outperforms state-of-the-art PFL methods, showing faster model training, increased robustness, and improved handling of data heterogeneity under different heterogeneous settings.

Moein Monemi, Morteza Amini, S. Mahmoud Taheri et al.

We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect of variance uncertainty in {their model}. We show that including the variance uncertainty can improve the prediction performance of the Bayesian {NN}. Variance uncertainty enhances the generalization of the model {by} considering the posterior distribution over the variance parameter. { We examine the generalization ability of the proposed model using a function approximation} example and {further illustrate it with} the riboflavin genetic data set. {We explore fully connected dense networks and dropout NNs with} Gaussian and spike-and-slab priors, respectively, for the network weights.

Majid Salehi, Morteza Amini

Widespread growth in Android malwares stimulates security researchers to propose different methods for analyzing and detecting malicious behaviors in applications. Nevertheless, current solutions are ill-suited to extract the fine-grained behavior of Android applications accurately and efficiently. In this paper, we propose ServiceMonitor, a lightweight host-based detection system that dynamically detects malicious applications directly on mobile devices. ServiceMonitor reconstructs the fine-grained behavior of applications based on a novel systematic system service use analysis technique. Using proposed system service use perspective enables us to build a statistical Markov chain model to represent what and how system services are used to access system resources. Afterwards, we consider built Markov chain in the form of a feature vector and use it to classify the application behavior into either malicious or benign using Random Forests classification algorithm. ServiceMonitor outperforms current host-based solutions with evaluating it against 4034 malwares and 10024 benign applications and obtaining 96\% of accuracy rate and negligible overhead and performance penalty.