Backdoor Attacks Research Articles

Defending against Backdoors in Federated Learning with Robust Learning Rate

Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. At a high level, our defense is based on carefully adjusting the aggregation server's learning rate, per dimension and per round, based on the sign information of agents' updates. We first conjecture the necessary steps to carry a successful backdoor attack in FL setting, and then, explicitly formulate the defense based on our conjecture. Through experiments, we provide empirical evidence that supports our conjecture, and we test our defense against backdoor attacks under different settings. We observe that either backdoor is completely eliminated, or its accuracy is significantly reduced. Overall, our experiments suggest that our defense significantly outperforms some of the recently proposed defenses in the literature. We achieve this by having minimal influence over the accuracy of the trained models. In addition, we also provide convergence rate analysis for our proposed scheme.

Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification

Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data. The backdoor can be activated when a normal input is stamped with a certain pattern called trigger, causing misclassification. Many existing trojan attacks have their triggers being input space patches/objects (e.g., a polygon with solid color) or simple input transformations such as Instagram filters. These simple triggers are susceptible to recent backdoor detection algorithms. We propose a novel deep feature space trojan attack with five characteristics: effectiveness, stealthiness, controllability, robustness and reliance on deep features. We conduct extensive experiments on 9 image classifiers on various datasets including ImageNet to demonstrate these properties and show that our attack can evade state-of-the-art defense.

Mitigating backdoor attacks in LSTM-based text classification systems by Backdoor Keyword Identification

It has been proved that deep neural networks are facing a new threat called backdoor attacks, where the adversary can inject backdoors into the neural network model through poisoning the training dataset. When the input containing some special pattern called the backdoor trigger, the model with backdoor will carry out malicious task such as misclassification specified by adversaries. In text classification systems, backdoors inserted in the models can cause spam or malicious speech to escape detection. Previous work mainly focused on the defense of backdoor attacks in computer vision, little attention has been paid to defense method for RNN backdoor attacks regarding text classification. In this paper, through analyzing the changes in inner LSTM neurons, we proposed a defense method called Backdoor Keyword Identification (BKI) to mitigate backdoor attacks which the adversary performs against LSTM-based text classification by data poisoning. This method can identify and exclude poisoning samples crafted to insert backdoor into the model from training data without a verified and trusted dataset. We evaluate our method on four different text classification datset: IMDB, DBpedia ontology, 20 newsgroups and Reuters-21578 dataset. It all achieves good performance regardless of the trigger sentences.

Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing

Backdoor data poisoning (a.k.a. Trojan attack) is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class. For a successful attack, during operation, the trained classifier will: 1) misclassify a test image from the source class(es) to the target class whenever the backdoor pattern is present; 2) maintain high classification accuracy for backdoor-free test images. In this paper, we make a breakthrough in defending backdoor attacks with imperceptible backdoor patterns (e.g. watermarks) before/during the classifier training phase. This is a challenging problem because it is a priori unknown which subset (if any) of the training set has been poisoned. We propose an optimization-based reverse engineering defense that jointly: 1) detects whether the training set is poisoned; 2) if so, accurately identifies the target class and the training images with the backdoor pattern embedded; and 3) additionally, reverse engineers an estimate of the backdoor pattern used by the attacker. In benchmark experiments on CIFAR-10 (as well as four other data sets), considering a variety of attacks, our defense achieves a new state-of-the-art by reducing the attack success rate to no more than 4.9% after removing detected suspicious training images.

Neural network laundering: Removing black-box backdoor watermarks from deep neural networks

Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into copyright protection for neural networks has been limited. One of the main methods for achieving such protection involves relying on the susceptibility of neural networks to backdoor attacks in order to inject a watermark into the network, but the robustness of these tactics has been primarily evaluated against pruning, fine-tuning, and model inversion attacks. In this work, we propose an offensive neural network “laundering” algorithm to remove these backdoor watermarks from neural networks even when the adversary has no prior knowledge of the structure of the watermark. We can effectively remove watermarks used for recent defense or copyright protection mechanisms while retaining test accuracies on the target task above 97% and 80% for both MNIST and CIFAR-10, respectively. For all watermarking methods addressed in this paper, we find that the robustness of the watermark is significantly weaker than the original claims. We also demonstrate the feasibility of our algorithm in more complex tasks as well as in more realistic scenarios where the adversary can carry out efficient laundering attacks using less than 1% of the original training set size, demonstrating that existing watermark-embedding procedures are not sufficient to reach their claims.

Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set.

Backdoor data poisoning attacks add mislabeled examples to the training set, with an embedded backdoor pattern, so that the classifier learns to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address posttraining detection of scene-plausible perceptible backdoors, a type of backdoor attack that can be relatively easily fashioned, particularly against DNN image classifiers. A post-training defender does not have access to the potentially poisoned training set, only to the trained classifier, as well as some unpoisoned examples that need not be training samples. Without the poisoned training set, the only information about a backdoor pattern is encoded in the DNN's trained weights. This detection scenario is of great import considering legacy and proprietary systems, cell phone apps, as well as training outsourcing, where the user of the classifier will not have access to the entire training set. We identify two important properties of scene-plausible perceptible backdoor patterns, spatial invariance and robustness, based on which we propose a novel detector using the maximum achievable misclassification fraction (MAMF) statistic. We detect whether the trained DNN has been backdoor-attacked and infer the source and target classes. Our detector outperforms existing detectors and, coupled with an imperceptible backdoor detector, helps achieve posttraining detection of most evasive backdoors of interest.

Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks

Trojan attacks on deep neural networks (DNNs) exploit a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backdoor</i> embedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploiting <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STR</u> ong <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ntentional <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> erturbation of inputs that is a multi-domain input-agnostic Trojan detection defence across <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vi</u> sion, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ext and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> udio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.

A Master Key backdoor for universal impersonation attack against DNN-based face verification

We introduce a new attack against face verification systems based on Deep Neural Networks (DNN). The attack relies on the introduction into the network of a hidden backdoor, whose activation at test time induces a verification error allowing the attacker to impersonate any user. The new attack, named Master Key backdoor attack, operates by interfering with the training phase, so to instruct the DNN to always output a positive verification answer when the face of the attacker is presented at its input. With respect to existing attacks, the new backdoor attack offers much more flexibility, since the attacker does not need to know the identity of the victim beforehand. In this way, he can deploy a Universal Impersonation attack in an open-set framework, allowing him to impersonate any enrolled users, even those that were not yet enrolled in the system when the attack was conceived. We present a practical implementation of the attack targeting a Siamese-DNN face verification system, and show its effectiveness when the system is trained on VGGFace2 dataset and tested on LFW and YTF datasets. According to our experiments, the Master Key backdoor attack provides a high attack success rate even when the ratio of poisoned training data is as small as 0.01, thus raising a new alarm regarding the use of DNN-based face verification systems in security-critical applications.

Backdoors hidden in facial features: a novel invisible backdoor attack against face recognition systems

Deep neural network (DNN) based face recognition system has become one of the most popular modalities for user identity authentication. However, some recent studies have indicated that, the malicious attackers can inject specific backdoors into the DNN model of a face recognition system, which is known as backdoor attack. As a result, the attacker can trigger the backdoors and impersonate someone else to log into the system, while not affecting the normal usage of the legitimate users. Existing studies use the accessories (such as purple sunglasses or bandanna) as the triggers of their backdoor attacks, which are visually conspicuous and can be easily perceptible by humans, thus result in the failure of backdoor attacks. In this paper, for the first time, we exploit the facial features as the carriers to embed the backdoors, and propose a novel backdoor attack method, named BHF2 (Backdoor Hidden in Facial Features). The BHF2 constructs the masks with the shapes of facial features (eyebrows and beard), and then injects the backdoors into the masks to ensure the visual stealthiness. Further, to make the backdoors look more natural, we propose BHF2N (Backdoor Hidden in Facial Features Naturally) method, which exploits the artificial intelligence (AI) based tool to auto-embed the natural backdoors. The generated backdoors are visually stealthy, which can guarantee the concealment of the backdoor attacks. The proposed methods (BHF2 and BHF2N) can be applied for those black-box attack scenarios, in which a malicious adversary has no knowledge of the target face recognition system. Moreover, the proposed attack methods are feasible for those strict identity authentication scenarios where the accessories are not permitted. Experimental results on two state-of-the-art face recognition models show that, the maximum success rate of the proposed attack method reaches 100% on DeepID1 and VGGFace models, while the accuracy degradation of target recognition models are as low as 0.01% (DeepID1) and 0.02% (VGGFace), respectively. Meantime, the generated backdoors can achieve visual stealthiness, where the pixel change rate of a backdoor instance relative to its clean face image is as low as 0.16%, and their structural and dHash similarity score are high up to 98.82% and 98.19%, respectively.

Stability-Based Analysis and Defense against Backdoor Attacks on Edge Computing Services

With the explosive development of mobile Internet and deep learning (DL), intelligent edge computing services based on collaborative learning are widely deployed in various application scenarios. These intelligent services include intelligent applications based on edge computing and DL-based optimization for edge computing (e.g., caching and communicating). However, in a wide variety of domains, DL has been found to be vulnerable to adversarial attacks, especially architecture-independent backdoor attacks. It embeds the attack pattern into the learned model and only performs the attack when it encounters the corresponding trigger. In this article, for the first time we analyze the impact of backdoor attacks on intelligent edge computing services. The simulation results demonstrate that once one or more edge nodes implement backdoor attacks, the embedded attack pattern will rapidly expand to all relevant edge nodes, which poses huge challenges to security-sensitive intelligent edge computing services. Subsequently, we analyze the trade-off between expected performance and ability to defend against backdoor attacks, which sheds new light on designing defense mechanisms for intelligent edge computing services. To address the challenges posed by backdoor attacks, we propose a stability-based defense mechanism. The experimental results demonstrate that the newly proposed defense mechanism can effectively defend against different levels of backdoor attacks without knowing whether there are adversaries, which is conducive to the deployment of the stability-based defense mechanism in real-world scenarios.

FREPD: A Robust Federated Learning Framework on Variational Autoencoder

Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in the federated learning setting. We propose a federated learning framework, which we call Federated Reconstruction Error Probability Distribution (FREPD). FREPD uses a VAE model to compute updates’ reconstruction errors. Updates with higher reconstruction errors than the average reconstruction error are deemed as malicious updates and removed. Meanwhile, we apply the Kolmogorov-Smirnov test to choose a proper probability distribution function and tune its parameters to fit the distribution of reconstruction errors from observed benign updates. We then use the distribution function to estimate the probability that an unseen reconstruction error belongs to the benign reconstruction error distribution. Based on the probability, we classify the model updates as benign or malicious. Only benign updates are used to aggregate the global model. FREPD is tested with extensive experiments on independent and identically distributed (IID) and non-IID federated benchmarks, showing a competitive performance over existing aggregation methods under Byzantine attack and backdoor attack.

Privacy-Enhanced Federated Learning Against Poisoning Adversaries

Federated learning (FL), as a distributed machine learning setting, has received considerable attention in recent years. To alleviate privacy concerns, FL essentially promises that multiple parties jointly train the model by exchanging gradients rather than raw data. However, intrinsic privacy issue still exists in FL, e.g., user’s training samples could be revealed by solely inferring gradients. Moreover, the emerging poisoning attack also poses a crucial security threat to FL. In particular, due to the distributed nature of FL, malicious users may submit crafted gradients during the training process to undermine the integrity and availability of the model. Furthermore, there exists a contradiction in simultaneously addressing two issues, that is, privacy-preserving FL solutions are dedicated to ensuring gradients indistinguishability, whereas the defenses against poisoning attacks tend to remove outliers based on their similarity. To solve such a dilemma, in this paper, we aim to build a bridge between the two issues. Specifically, we present a privacy-enhanced FL (PEFL) framework that adopts homomorphic encryption as the underlying technology and provides the server with a channel to punish poisoners via the effective gradient data extraction of the logarithmic function. To the best of our knowledge, the PEFL is the first effort to efficiently detect the poisoning behaviors in FL under ciphertext. Detailed theoretical analyses illustrate the security and convergence properties of the scheme. Moreover, the experiments conducted on real-world datasets show that the PEFL can effectively defend against label-flipping and backdoor attacks, two representative poisoning attacks in FL.

Text Backdoor Detection Using an Interpretable RNN Abstract Model

Deep neural networks (DNNs) are known to be inherently vulnerable to malicious attacks such as the adversarial attack and the backdoor attack. The former is crafted by adding small perturbations to benign inputs so as to fool a DNN. The latter generally embeds a hidden pattern in a DNN by poisoning the dataset during the training process, which causes the infected model to misbehave on predefined inputs with a specific trigger and normally perform for others. Much work has been conducted on defending against the adversarial samples, while the backdoor attack received much less attention, especially in recurrent neural networks (RNNs), which play an important role in the text processing field. Two main limitations make it hard to directly apply existing image backdoor detection approaches to RNN-based text classification systems. First, a layer in an RNN does not preserve the same feature latent space function for different inputs, making it impossible to map the inserted specific pattern with the neural activations. Second, the text data is inherently discrete, making it hard to optimize the text like image pixels. In this work, we propose a novel backdoor detection approach named InterRNN for RNN-based text classification systems from the interpretation perspective. Specifically, we first propose a novel RNN interpretation technique by constructing a nondeterministic finite automaton (NFA) based abstract model, which can effectively reduce the analysis complexity of an RNN while preserving its original logic rules. Then, based on the abstract model, we can obtain interpretation results that explain the fundamental reason behind the decision for each input. We then detect trigger words by leveraging the differences between the behaviors in the backdoor sentences and those in the normal sentences. The extensive experiment results on four benchmark datasets demonstrate that our approach can generate better interpretation results compared to state-of-the-art approaches and effectively detect backdoors in RNNs.

Use Procedural Noise to Achieve Backdoor Attack

In recent years, more researchers pay their attention to the security of artificial intelligence. The backdoor attack is one of the threats and has a powerful, stealthy attack ability. There exist a growing trend towards the triggers is that become dynamic and global. In this paper, we propose a novel global backdoor trigger that is generated by procedural noise. Compared with most triggers, ours are much stealthy and straightforward to implement. In fact, there exist three types of procedural noise, and we evaluate the attack ability of triggers generated by them on the different classification datasets, including CIFAR-10, GTSRB, CelebA, and ImageNet12. The experiment results show that our attack approach can bypass most defense approaches, even for the inspections of humans. We only need poison 5%–10% training data, but the attack success rate(ASR) can reach over 99%. To test the robustness of the backdoor model against the corruption methods that in practice, we introduce 17 corruption methods and compute the accuracy, ASR of the backdoor model with them. The facts show that our backdoor model has strong robustness for most corruption methods, which means it can be applied in reality. Our code is available at https://github.com/928082786/pnoiseattack .

Exploring adversarial attacks against malware classifiers in the backdoor poisoning attack

Machine learning is becoming the latest trend by making growing use of it in various fields of application. Along with that, the invasions designed to evade these systems has also evolved apparently. The adversarial attacks are becoming the major threats for developing threats to these models. Current training pipelines for the classification of malware based on machine learning (ML) depend on crowdsourced threat feeds which expose a normal point of injection. We research the vulnerability of ML malware classifications to backdoor poisoning attack for the first time, concentrating explicitly on stimulating “clean label” attacks where attackers donate monitor the mechanism of sample classification. In this paper, we reviewed various attacks based on machine learning models and their working strategies. We also discussed the threat models and backdoor attacks on malware classifiers.

Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based attacks. In this work, we explore the backdooring/trojanning of DRL-based AV controllers. We develop a trigger design methodology that is based on well-established principles of traffic physics. The malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge (congestion attacks) or AV acceleration resulting in the AV crashing into the vehicle in front (insurance attack). We test our attack on single-lane and two-lane circuits. Our experimental results show that the backdoored model does not compromise normal operation performance, with the maximum decrease in cumulative rewards being 1%. Still, it can be maliciously activated to cause a crash or congestion when the corresponding triggers appear.

Detection of Backdoors in Trained Classifiers Without Access to the Training Set.

With wide deployment of deep neural network (DNN) classifiers, there is great potential for harm from adversarial learning attacks. Recently, a special type of data poisoning (DP) attack, known as a backdoor (or Trojan), was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a target class t∗ whenever the backdoor pattern is present in a test example originally from a source class s∗ . Launching backdoor attacks does not require knowledge of the classifier or its training process-only the ability to poison the training set with exemplars containing a backdoor pattern (labeled with the target class). Defenses against backdoors can be deployed before/during training, post-training, or at test time. Here, we address post-training detection in DNN image classifiers, seldom considered in existing works, wherein the defender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean (unpoisoned) examples from the classification domain. This scenario is of great interest because e.g., a classifier may be the basis of a phone app that will be shared with many users. Detection may thus reveal a widespread attack. We propose a purely unsupervised anomaly detection (AD) defense against imperceptible backdoor attacks that: 1) detects whether the trained DNN has been backdoor-attacked; 2) infers the source and target classes in a detected attack; 3) estimates the backdoor pattern itself. Our AD approach involves learning (via suitable cost function minimization) the minimum size/norm perturbation (putative backdoor) required to induce the classifier to misclassify (most) examples from class s to class t , for all (s,t) pairs. Our hypothesis is that nonattacked pairs require large perturbations, while the attacked pair (s∗, t∗) requires much smaller ones. This is convincingly borne out experimentally. We identify a variety of plausible cost functions and devise a novel, robust hypothesis testing approach to perform detection inference. We test our approach, in comparison with the state-of-the-art methods, for several backdoor patterns, attack settings and mechanisms, and data sets and demonstrate its favorability. Our defense essentially requires setting a single hyperparameter (the detection threshold), which can e.g., be chosen to fix the system's false positive rate.

Study of scale-free structures in feed-forward neural networks against backdoor attacks

Due to the computational complexities of artificial neural networks, MLaaS (machine learning as a service), which is one of the main cloud computing services, is taking the responsibility of the neural network training. With the increase in demand for third-party neural network training, there is a high possibility of adversarial attacks through malicious training. Backdoor attacks are among the most efficient attacks which cause targeted misclassification while the accuracy on clean data is not affected. In this paper, we provide the first investigation about the influence of applying scale-free networks to feed-forward neural networks (FFNNs) against backdoor attacks inserted via the MNIST dataset. It is the first time that the feed-forward neural network structure is changed to improve the network robustness against backdoor attacks using scale-free structure before the network is getting attacked. It has been achieved that scale-free neural networks with long range connections not only keep the accuracy high with strong stability but also make it independent of the number of hidden layers and prevent overfitting.

Defense against neural trojan attacks: A survey

Deep learning techniques have become significantly prevalent in many real-world problems including a variety of detection, recognition, and classification tasks. To obtain high-performance neural networks, an enormous amount of training datasets, memory, and time-consuming computations are required which has increased the demands for outsource training among users. As a result, the machine-learning-as-a-service(MLaaS) providers or a third party can gain an opportunity to put the model’s security at risk by training the model with malicious inputs. The malicious functionality inserted into the neural network by the adversary will be activated in the presence of specific inputs. These kinds of attacks to neural networks, called trojan or backdoor attacks, are very stealthy and hard to detect because they do not affect the network performance on clean datasets. In this paper, we refer to two important threat models and we focus on the detection and mitigation techniques against these types of attacks on neural networks which has been proposed recently. We summarize, discuss, and compare the defense methods and their corresponding results.

A survey on security and privacy of federated learning

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon decentralized data and training that brings learning to the edge or directly on-device. FL is a new research area often referred to as a new dawn in AI, is in its infancy, and has not yet gained much trust in the community, mainly because of its (unknown) security and privacy implications. To advance the state of the research in this area and to realize extensive utilization of the FL approach and its mass adoption, its security and privacy concerns must be first identified, evaluated, and documented. FL is preferred in use-cases where security and privacy are the key concerns and having a clear view and understanding of risk factors enable an implementer/adopter of FL to successfully build a secure environment and gives researchers a clear vision on possible research areas. This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible. We present an illustrative description of approaches and various implementation styles with an examination of the current challenges in FL and establish a detailed review of security and privacy concerns that need to be considered in a thorough and clear context. Findings from our study suggest that overall there are fewer privacy-specific threats associated with FL compared to security threats. The most specific security threats currently are communication bottlenecks, poisoning, and backdoor attacks while inference-based attacks are the most critical to the privacy of FL. We conclude the paper with much needed future research directions to make FL adaptable in realistic scenarios.