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In the federated learning (FL) research field, current research is confronted with several pivotal challenges, e.g., data privacy, model utility and communication efficiency. Furthermore, these challenges are further amplified by statistical data ...

• An end-to-end local adaptive compressed learning strategy is designed.
• Just a small amount of noise can provide stronger data privacy guarantee.
• Additional heterogeneity induced by Gaussian differential privacy can be mitigated.

Federated learning is a distributed learning approach aimed at preserving user’s data privacy, while incremental learning is an adaptive machine learning method that enables continuous learning of new data. The combination of these two approaches ...

• Proposes a topological stability loss function based on Topological Data Analysis (TDA).
• Presenting a federated incremental learning framework that incorporates the detection of new class tasks based on changes in the local model’s ...

Federated learning enables multi-party model training by utilizing shared models instead of raw data, allowing for effective use of user data while ensuring privacy protection. However, the training process still has potential threats. Guided by ...

Federated learning is an efficient machine learning tool for dealing with heterogeneous big data and privacy protection. Federated learning methods with regularization can control the level of communications between the central and local ...

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, ...

• This paper proposes a typical process for establishing FLIDS, summarized into six key research content.
• Based on these findings, research topics are derived and studies are surveyed, focusing on key research contents.
• Discussed ...

Federated learning (FL) allows participants to jointly train a model without leaking their sensitive datasets. The server is designed to have no visibility into how these updates are generated for privacy protection. Despite its benefits, FL is ...

Knowledge graphs (KGs) are often incomplete, omitting many existing facts. To address this issue, researchers have proposed many knowledge graph completion (KGC) models to fill in the missing triples. A full KG often consists of interconnected ...

The label distribution skew has been shown to be a significant obstacle that limits the model performance in federated learning (FL). This challenge could be more serious when the participating clients are in unstable network circumstances and ...

With the increasing awareness of data privacy protection and the growing stringency of data security regulations, federated learning (FL) as a distributed machine learning approach has garnered widespread attention. However, in practice, FL faces ...

The aim of heterogeneous federated learning (HFL) is to address the issues of data heterogeneity, computational resource disparity, and model generalizability and security in federated learning (FL). To facilitate the collaborative training of ...

Similar to traditional machine learning, federated learning is susceptible to adversarial attacks. Existing defense methods against federated attacks often rely on extensive labeling during the local training process to enhance model robustness. ...$\mathbf{}\mathbf{}\mathbf{}\mathbf{}$

This paper introduces a novel approach for training generative adversarial networks using federated machine learning. Generative adversarial networks have gained plenty of attention in the research community especially with their abilities to ...

The scarcity of medical images significantly hampers the advancement of artificial intelligence techniques in the medical field. Medical images face the issue of inferior training accuracy and efficiency in classification tasks due to insufficient ...

Federated learning (FL) enables resource-constrained node devices to learn a shared model while keeping the training data local. Since recent research has demonstrated multiple privacy leakage attacks in FL, e.g., gradient inference attacks and membership ...

Due to the high cost of labeling and the high requirements of annotation professionalism, there is a lack of labeling of large quantities of data. As a solution to the problem of partially labeled data in federated learning (FL), federated semi-...

With the development of edge computing and Internet of Things (IoT), the computing power of edge devices continues to increase, and the data obtained is more specific and private. Methods based on Federated Learning (FL) can help utilize the data ...

Federated learning often assumes that data is fixed in advance, which is unrealistic in many real-world scenarios where new data continuously arrives, causing catastrophic forgetting. To address this challenge, we propose FCLPF (Federated Class ...

Insider threats in Internet of Things (IoT) networks pose significant risks, as compromised devices can misuse their privileges to cause substantial harm. Centralized methods for insider threat detection in IoT devices are critical for identifying ...

Brain tumours are abnormal growths of cells within the brain or the central spinal canal. The occurrence of this disease in a critical location can cause significant neurological complications. Accurate and early brain tumour detection is required ...

Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is ...