Personalized federated learning trains personalized models tailored to meet individual client’s specific data distributions. However, global models often introduce irrelevant information into personalized models, reducing communication efficiency ...

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• Propose a bi-component framework for client aggregation and clustering.
• Combines global and mini-federations for enhanced personalization.
• MiniPFL cuts communication rounds by 30%, excelling in performance.

Multi-task reinforcement learning and meta-reinforcement learning have been developed to enhance the learning capabilities on new tasks, but they tend to focus on maximizing rewards, leading to poor performance in exploration. To address this ...

• A method is proposed to boost the exploration performance of reinforcement learning on new tasks.
• A personalized method is proposed to boost reinforcement learning on distinct tasks.
• The convergence analysis is established for the ...

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs ...

This paper introduces a pioneering framework for music representation learning, which harnesses knowledge graph embeddings to enrich genre classification. Leveraging metadata from publicly available datasets like FMA and OpenMIC-2018, the constructed ...

Recent defense approaches against targeted model poisoning attacks aim to prevent specific prediction failures in federated learning (FL). However, these defenses remain susceptible to targeted collusion attacks, particularly under conditions of high ...

Cerebrovascular (i.e., cerebral vessel) segmentation is essential for diagnosing and treating brain diseases. Convolutional neural network models, such as U-Net, are commonly used for this purpose. Unfortunately, such models may not be entirely ...

• A transferability framework is designed to address the small amount of private clinical cerebrovascular datasets.
• Exploration of adaptive strategies for different modalities of cerebrovascular datasets (MRI-CT).
• Extracting ...

Clustered federated learning (CFL) has proved to be an effective way to alleviate the non-IID (not independently and identically distributed) data challenge, which severely restricts the wider application of federated learning. However, existing ...

Federated Learning (FL) always involves a large number of heterogeneous clients (both in statistic and resource heterogeneity) when collaboratively training a model, leading to a compromise in the model performance. Recent research has ...

The ubiquity of illumination facilities enables the versatile development of Visible Light Communication (VLC). VLC-based research achieved high-speed wireless access and decimeter-level indoor localization with complex equipment. However, it is still ...

Different from intermittent ISM signals like Bluetooth and WiFi, LTE signals are continuous in time and more pervasive in space, which makes them suitable carriers for ambient backscatter systems. However, due to the continuous LTE traffic and complex ...

Federated domain adaptation (FDA) is an effective method for performing learning tasks over distributed networks, which well improves data privacy and portability in unsupervised multi-source domain adaptation (UMDA) tasks. Despite the impressive gains ...

Estimating 3D human mesh is appealing for various application scenarios. Current mainstream solution predicts the meshes either from the image or the human reflected RF-signals. In this paper, instead of investigating which approach is better, we propose ...

Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in ...

Hierarchical federated learning (HFL) allows multiple edge aggregations at edge devices before one global aggregation to address both issues of non-independent and identically distributed (non-IID) data and communication bottleneck in ...

• FedPEC breaks the assumption that clients can be assigned to any edge device.
• ...

In this paper, we observe an acoustic covert channel by modulating capacitor squeal on the monitor's power supply unit, and then present ScreenInformer to build a covert communication within a physically isolated network system. Unlike traditional ...

Speech enhancement can benefit lots of practical voice-based interaction applications, where the goal is to generate clean speech from noisy ambient conditions. This paper presents a practical design, namely UltraSpeech, to enhance speech by exploring ...

Traditional multi-objective reinforcement learning problems pay attention to the expected return of each objective under different preferences. However, the difference in strategy in practice is also important. This paper proposes an algorithm ...

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal ...

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• This work is inspired by recent studies on deep learning with noisy label.
• The ...

Unsupervised domain adaptation (UDA) algorithms aim to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. To tackle this issue, domain-alignment models based on different representation space ...

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs ...