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Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression ...

Federated learning (FL) enables edge devices to cooperatively train models without exposing their raw data. However, implementing a practical FL system at the network edge mainly faces three challenges: label noise, data non-IIDness, and device ...

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) aims to bridge the domain gap by transferring knowledge from the labeled source domain to the unlabeled target domain. Recently, pseudo-label-based approaches have become ...

• We propose an attention-disentangled re-ID network to explore more discriminative feature representations to resolve the contradiction between intra-class diversity and the stability of pseudo labels.
• We propose a spatial attention-...

Federated semi-supervised learning (FSSL) has been proposed to address the insufficient labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a single global model is always trained without an equivalent generalization ...

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis ...

• Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity.
• Adopt an opinion modification mechanism based on social network.
• The MECMs with uncertain adjustment costs are developed under three ...

In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the ...

Data generated at the network edge can be processed locally by leveraging the emerging technology of Federated Learning (FL). However, non-IID local data will lead to degradation of model accuracy and the heterogeneity of edge nodes inevitably slows down ...

Enhancing privacy on smart contract-enabled blockchains has garnered much attention in recent research. Zero-knowledge proofs (ZKPs) is one of the most popular approaches, however, they fail to provide full expressiveness and fine-grained privacy. To ...

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server based federated ...

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, the data on each client may be insufficient for model ...

At the network edge, federated learning (FL) has gained attention as a promising approach for training deep learning (DL) models collaboratively across a large number of devices while preserving user privacy. However, FL still faces specific challenges ...

Recently, mobile and embedded devices worldwide generate a massive amount of data at the network edge. To efficiently exploit the data from distributed devices, we concentrate on decentralized machine learning (DML), where the workers collaboratively ...

Data generated at the network edge can be processed locally by leveraging the paradigm of Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a practical and popular approach for distributed machine learning over locally ...

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively train AI models without exposing local data. In order to avoid the possible bottleneck of the parameter server (PS) architecture, we concentrate on the ...

Recently, edge AI has been launched to mine and discover valuable knowledge at network edge. Federated Learning, as an emerging technique for edge AI, has been widely deployed to collaboratively train models on many end devices in data-parallel fashion. ...

Recent advances in deep reinforcement learning (DRL) takes artificial intelligence to the next level, from making individual decisions to accomplishing sophisticated tasks via sequential decision makings, such as defeating world-class human players in ...

We propose the first federated correlation test framework compatible with secure aggregation, namely FED-χ². In our protocol, the statistical computations are recast as frequency moment estimation problems, where the clients collaboratively generate a ...

In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported when the model is pretrained on public data. Some gain is expected as these models inherit the benefits of ...

The increasing pace of data collection has led to increasing awareness of privacy risks, resulting in new data privacy regulations like General data Protection Regulation (GDPR). Such regulations are an important step, but automatic compliance ...

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of ...