Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the risk of introducing detrimental features generated by AI and harming downstream tasks. This paper identifies the frequency misalignment between real and synthesized images as one of the key factors underlying unreliable GDA and proposes the Frequency Recalibration (FreRec) method to reduce the frequency distributional discrepancy and thus improve GDA. FreRec involves (1) Statistical High-frequency Replacement (SHR) to roughly align high-frequency components and (2) Reconstructive High-frequency Mapping (RHM) to enhance image quality and reconstruct high-frequency details. Extensive experiments were conducted in various medical datasets, including brain MRIs, chest X-rays, and fundus images. The results show that FreRec significantly improves downstream medical image classification performance compared to uncalibrated AI-synthesized samples. FreRec is a standalone post-processing step that is compatible with any generative model and can integrate seamlessly with common medical GDA pipelines.

The utility of multiple reinforcement learning (RL) agents collaboratively training within a shared environment, all working towards common objectives, is increasingly evident within the Internet of Vehicles (IoV). The multi-agent Advantage Actor-Critic (MA2C) algorithm is a prominent example of such a Multi-Agent Reinforcement Learning (MARL) system. However, MA2C requires agents to share policies, such as pairs of states and actions and even trained models, among neighboring agents, to overcome the challenge of agents having only partial observations. Unfortunately, this requirement amplifies the communication overhead and raises privacy concerns. Federated learning (FL), as a privacy-preserving machine learning method, can be applied in the MARL context with a central server aggregating the weights of the agents' models. However, this technique assumes that all agents are capable of executing identical actions, which may be impractical. In this paper, we introduce a novel FL A2C algorithm called Advantage Actor Federated Critic (A2FC). The proposed algorithm streamlines the aggregation of agents' critic models while offloading the training of actor models to the individual agents' local machines. An experiment conducted in an adaptive traffic signal control (ATSC) system demonstrates the method's effectiveness in personalizing agents' actions, preserving agents' privacy during training, and mitigating communication overhead issues.

In federated learning (FL), local models are trained independently by clients, local model parameters are shared with a global aggregator or server, and then the updated model is used to initialize the next round of local training. FL and its variants have become synonymous with privacy-preserving distributed machine learning. However, most FL methods have maximization of model accuracy as their sole objective, and rarely are the clients’ needs and constraints considered. In this paper, we consider that clients have differing performance expectations and resource constraints, and we assume local data quality can be improved at a cost. In this light, we treat FL in the training phase as a game in satisfaction form that seeks to satisfy all clients’ expectations. We propose two novel FL methods, a deep reinforcement learning method and a stochastic method, that embrace this design approach. We also account for the scenario where certain clients can adjust their actions even after being satisfied, by introducing probabilistic parameters in both of our methods. The experimental results demonstrate that our proposed methods converge quickly to a lower cost solution than competing methods. Furthermore, it was found that the probabilistic parameters facilitate the attainment of satisfaction equilibria (SE), addressing scenarios where reaching SEs may be challenging within the confines of traditional games in satisfaction form.

GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable features such as frequency artifacts, which will cause failures when facing unknown GANs or perturbation attacks. To overcome the issue, we propose a robust detection framework based on a novel multi-view image completion representation. The framework first learns various view-to-image tasks to model the diverse distributions of genuine images. Frequency-irrelevant features can be represented from the distributional discrepancies characterized by the completion models, which are stable, generalized, and robust for detecting unknown fake patterns. Then, a multi-view classification is devised with elaborated intra- and inter-view learning strategies to enhance view-specific feature representation and cross-view feature aggregation, respectively. We evaluated the generalization ability of our framework across six popular GANs at different resolutions and its robustness against a broad range of perturbation attacks. The results confirm our method's improved effectiveness, generalization, and robustness over various baselines.

Summary Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.

Summary Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.

Planning is one of the main approaches used to improve agents’ working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private information leaked. This article proposes a novel strong privacy-preserving planning approach for logistic-like problems. This approach outperforms existing approaches by addressing two challenges: 1) simultaneously achieving strong privacy; completeness and efficiency; and 2) addressing communication constraints. These two challenges are prevalent in many real-world applications including logistics in military environments and packet routing in networks. To tackle these two challenges, our approach adopts the differential privacy technique, which can both guarantee strong privacy and control communication overhead. To the best of our knowledge, this article is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems. We theoretically prove the strong privacy and completeness of our approach and empirically demonstrate its efficiency. We also theoretically analyze the communication overhead of our approach and illustrate how differential privacy can be used to control it.

Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record’s membership in a dataset or even reconstruct this data record using a confidence score vector predicted by the target model. However, most existing defense methods only protect against membership inference attacks. Methods that can combat both types of attacks require a new model to be trained, which may not be time-efficient. In this paper, we propose a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one parameter, the privacy budget. The central idea is to modify and normalize the confidence score vectors with a differential privacy mechanism which preserves privacy and obscures membership and reconstructed data. Moreover, this method can guarantee the order of scores in the vector to avoid any loss in classification accuracy. The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.

Cyber deception is one of the key approaches used to mislead attackers by hiding or providing inaccurate system information. There are two main factors limiting the real-world application of existing cyber deception approaches. The first limitation is that the number of systems in a network is assumed to be fixed. However, in the real world, the number of systems may be dynamically changed. The second limitation is that attackers' strategies are simplified in the literature. However, in the real world, attackers may be more powerful than theory suggests. To overcome these two limitations, we propose a novel differentially private game theoretic approach to cyber deception. In this proposed approach, a defender adopts differential privacy mechanisms to strategically change the number of systems and obfuscate the configurations of systems, while an attacker adopts a Bayesian inference approach to infer the real configurations of systems. By using the differential privacy technique, the proposed approach can 1) reduce the impacts on network security resulting from changes in the number of systems and 2) resist attacks regardless of attackers' reasoning power. The experimental results demonstrate the effectiveness of the proposed approach.