Backdoor attacks pose severe security challenges to federated learning systems due to their stealthy nature. Existing detection methods primarily focus on identifying anomalies by analyzing discrepancies in client model updates. However, in federated learning, the non-independent and identically distributed (non-IID) nature of client data leads to inconsistencies among local model updates, which can mask the distinguishing features of backdoor attacks and consequently degrade the performance of detection methods. Unlike benign models, which are trained solely for a single classification task, backdoored models are simultaneously optimized for both the classification (main) task and the backdoor task. Therefore, training the backdoored models can be regarded as a multi-task learning problem. Inspired by information bottleneck theory, we observe that backdoored models exhibit more stable feature representations than benign models when performing the main task. Based on this insight, we propose a novel stability metric that quantitatively captures the disparity in feature map stability between backdoored and benign models. Leveraging this metric, we develop a new backdoor detection framework for federated learning. Our method computes anomaly scores for each client and selectively aggregates models with benign characteristics, effectively defending against backdoor attacks. We validate our approach through extensive experiments on multiple benchmark datasets under non-IID settings. The results demonstrate that our method consistently achieves high detection performance across a range of backdoor scenarios and data heterogeneity levels.

Federated learning is vulnerable to backdoor attacks due to its distributed nature and the inability to access local datasets. Meanwhile, the heterogeneity of distributed data further complicates the detection of such attacks. However, existing defense strategies often overlook the presence of non-stationary objectives and noisy gradients across multiple clients, making it challenging to accurately and efficiently identify malicious participants. To address these challenges, we propose a backdoor defense method for Federated Learning with Adam optimizer and multi-granularity Clustering (FLAC), incorporating both coarse-grained and fine-grained clustering mechanisms to neutralize backdoor attacks. First, the Adam optimizer accelerates the learning process by mitigating the impact of noisy gradients and addressing the non-stationary objectives posed by different clients under attack. Second, a multi-granularity clustering process is considered to differentiate between benign clients and potential attackers. This is followed by an adaptive clipping strategy to further alleviate the influence of malicious attackers. Our theoretical analysis demonstrates the consistent convergence of Adam in a federated backdoor defense environment. Extensive experimental results validate the effectiveness of our defense approach.

The rapid growth of industrial Internet of Things (IoT) devices presents significant opportunities for federated learning to advance secure machine learning in industrial IoT systems. However, the performance of global federated learning models deteriorates significantly due to data heterogeneity inherent in industrial IoT systems. To address this issue, we propose a novel personalized federated learning framework via fine-grained one-shot clustering (FedFOC). Our method simultaneously considers the dataset structure and the relative positions of samples to extract compact features for clustering. Specifically, we combine clients’ locally calculated sample-level distances with MinHash signatures of their label sets to transform their data distributions into 1-D vectors to reduce communication costs. These vectors are then clustered to identify the fine-grained similarities between clients’ data distributions. To capture the complex relationships among local data distributions, we perform soft clustering in a one-shot manner and quantify client cluster association strengths. Subsequently, the clustering results are utilized to improve the local models’ personalized performance. Extensive experiments conducted on four widely used public datasets under various heterogeneous scenarios demonstrate that FedFOC outperforms state-of-the-art personalized federated learning methods in the accuracy of local models in almost all scenarios partitioned by Dirichlet. In addition, we also validate the effectiveness of our approach on the industrial dataset, indicating that our method has potential value in real-world applications.

3D lane detection is a fundamental task in autonomous driving, and detecting 3D lanes from monocular images has been widely adopted due to the low computational cost and the property of lanes. Recent progresses have been made based on surrogate representations such as bird’s eye view (BEV) features. However, monocular BEV construction strictly relies on flat groud assumption, and the misalignment between perspective view and BEV is inevitable. In this paper, we propose Ada3DLane, a BEV-free, query based 3D lane detector, which adaptively generates queries with rich semantic and geometric information as well as lane interactions; adaptively samples perspective view image features in spatial and temporal domain; adaptively decode the sampled features for fast and accurate 3D lane detection. We conduct extensive experiments on benchmark lane detection datasets and outperforms previous state-of-the-art methods.

Social Networking Services (SNS) are one of the most widely used web applications and have a profound influence on the real world. Network structure analysis plays a crucial role in the optimization of the functionalities of SNS. In particular, subgraph statistics is widely used as a fundamental method to reveal the basic features and patterns of networks. However, one of the main challenges in data collection is how to perform effective analysis without violating users’ privacy. Existing privacy-preserving subgraph statistics methods mainly rely on enumeration, which are often inefficient or require multiple rounds of interactions. It is difficult to meet the needs of rapidly evolving social network services. To address this problem, we propose a set of new algorithms that aim to improve the efficiency and accuracy of subgraph counting under privacy preservation. By focusing on two typical network structures, the Random network and Barabási-Albert network, our approach is able to efficiently analyse the network structure while ensuring individual privacy. These algorithms perform with high accuracy in experiments, especially in evaluating triangles in BA networks, when compared with the state-of-the-art algorithm.

Federated learning (FL) is a privacy-aware computing framework that enables multiple clients to collaborate in solving machine learning problems. In real scenarios, non-IID data held by different edge devices will degrade the performance of global FL models. To address this issue, most FL methods utilize cluster algorithms to group clients with similar distributions. However, these methods do not fully utilize the distribution features of client data, resulting in a lack of generalization in the cluster model. In order to make the cluster more suitable for the distribution features of user data, we propose a clustering-ensemble based federated learning method (FedCE) that sets each client associated with multiple clusters. We extract the features of client distributions to quantify the relationship between clients and clusters, and optimize the local model of clients through the historical performance of the cluster model. Furthermore, we dynamically estimate the number of clusters each client belongs to through the diversity of client performance. We conduct experiments on scenarios with mixture two-distributions, three-distributions and Dirichlet-distributions. The results show that the FedCE algorithm has better performance than the state-of-the-art clustered FL methods in both cluster and client models under different data distributions.

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

Social relations exist in real life widely, which can be abstracted into graphs to show various social networks. Community detection in social networks has become an important and effective methodology to understand the structure and function of real world networks. In this paper, the algorithm we proposed fully considers the influence of direct nodes and indirect nodes and expresses the relationship between users comprehensively. The algorithm runs on the social network graph based on a new granular computing method. Experimental results on bench mark data show the superiority of the proposed algorithm compared to other well-known methods.