Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference

This paper explores a star-of-star topology for an internet-of-things (IoT) network using mega low Earth orbit constellations where the IoT users broadcast their sensed information to multiple satellites simultaneously over a shared channel. The satellites use amplify-and-forward relaying to forward the received signal to the ground station (GS), which then combines them coherently using maximal ratio combining. A comprehensive outage probability (OP) analysis is performed for the presented topology. Stochastic geometry is used to model the random locations of satellites, thus making the analysis general and independent of any constellation. The satellites are assumed to be visible if their elevation angle is greater than a threshold, called a mask angle. Statistical characteristics of the range and the number of visible satellites are derived for a given mask angle. Successive interference cancellation (SIC) and capture model (CM)-based decoding schemes are analyzed at the GS to mitigate interference effects. The average OP for the CM-based scheme, and the OP of the best user for the SIC scheme are derived analytically. Simulation results are presented that corroborate the derived analytical expressions. Moreover, insights on the effect of various system parameters like mask angle, altitude, number of satellites and decoding order are also presented. The results demonstrate that the explored topology can achieve the desired OP by leveraging the benefits of multiple satellites. Thus, this topology is an attractive choice for satellite-based IoT networks as it can facilitate burst transmissions without coordination among the IoT users.Comment: Submitted to IEEE IoT Journa

Fair Differentially Private Federated Learning Framework

Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. While FL promotes privacy by minimizing the amount of user data stored on central servers, it still poses privacy risks that need to be addressed. Industry standards such as differential privacy, secure multi-party computation, homomorphic encryption, and secure aggregation protocols are followed to ensure privacy in FL. Fairness is also a critical issue in FL, as models can inherit biases present in local datasets, leading to unfair predictions. Balancing privacy and fairness in FL is a challenge, as privacy requires protecting user data while fairness requires representative training data. This paper presents a "Fair Differentially Private Federated Learning Framework" that addresses the challenges of generating a fair global model without validation data and creating a globally private differential model. The framework employs clipping techniques for biased model updates and Gaussian mechanisms for differential privacy. The paper also reviews related works on privacy and fairness in FL, highlighting recent advancements and approaches to mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires careful consideration of specific contexts and requirements, taking into account the latest developments in industry standards and techniques.Comment: Paper report for WASP module

Exploring privacy-preserving models in model space

Privacy-preserving techniques have become increasingly essential in the rapidly advancing era of artificial intelligence (AI), particularly in areas such as deep learning (DL). A key architecture in DL is the Multilayer Perceptron (MLP) network, a type of feedforward neural network. MLPs consist of at least three layers of nodes: an input layer, hidden layers, and an output layer. Each node, except for input nodes, is a neuron with a nonlinear activation function. MLPs are capable of learning complex models due to their deep structure and non-linear processing layers. However, the extensive data requirements of MLPs, often including sensitive information, make privacy a crucial concern. Several types of privacy attacks are specifically designed to target Deep Learning learning (DL) models like MLPs, potentially leading to information leakage. Therefore, implementing privacy-preserving approaches is crucial to prevent such leaks. Most privacy-preserving methods focus either on protecting privacy at the database level or during inference (output) from the model. Both approaches have practical limitations. In this thesis, we explore a novel privacy-preserving approach for DL models which focuses on choosing anonymous models, i.e., models that can be generated by a set of different datasets. This privacy approach is called Integral Privacy (IP). IP provide sound defense against Membership Inference Attacks (MIA), which aims to determine whether a sample was part of the training set. Considering the vast number of parameters in DL models, searching the model space for recurring models can be computationally intensive and time-consuming. To address this challenge, we present a relaxed variation of IP called $\Delta$-Integral Privacy ($\Delta$-IP), where two models are considered equivalent if their difference is within some $\Delta$ threshold. We also highlight the challenge of comparing two DNNs, particularly when similar layers in different networks may contain neurons that are permutations or combinations of one another. This adds complexity to the concept of IP, as identifying equivalencies between such models is not straightforward. In addition, we present a methodology, along with its theoretical analysis, for generating a set of integrally private DL models. In practice, data often arrives rapidly and in large volumes, and its statistical properties can change over time. Detecting and adapting to such drifts is crucial for maintaining model's reliable prediction over time.  Many approaches for detecting drift rely on acquiring true labels, which is often infeasible. Simultaneously, this exposes the model to privacy risks, necessitating that drift detection be conducted using privacy-preserving models. We present a methodology that detects drifts based on uncertainty in predictions from an ensemble of integrally private MLPs. This approach can detect drifts even without access to true labels, although it assumes they are available upon request.  Furthermore, the thesis also addresses the membership inference concern in federated learning for computer vision models. Federated Learning (FL) was introduced as privacy-preserving paradigm in which users collaborate to train a joint model without sharing their data. However, recent studies have indicated that the shared weights in FL models encode the data they are trained on, leading to potential privacy breaches. As a solution to this problem, we present a novel integrally private aggregation methodology for federated learning along with its convergence analysis.