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Works196 from public data
- SplitFed: When Federated Learning Meets Split Learning1,007
A novel approach is presented, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness.
- A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT868
This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimmodal interaction and multimodal interaction, and introduces the generation tasks and relative models of text and image.
- Membership Inference Attacks on Machine Learning: A Survey746
This article provides the taxonomies for both attacks and defenses, based on their characterizations, and discusses their pros and cons, and point out several promising future research directions to inspire the researchers who wish to follow this area.
- Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models734
This paper presents a comprehensive review of the model's background, related technologies, applications, remaining challenges, and future directions of text-to-video AI models.
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- Privacy and Robustness in Federated Learning: Attacks and Defenses582
A comprehensive survey on privacy and robustness in FL over the past five years is conducted and a concise introduction to the concept of FL is provided and a unique taxonomy covering privacy attacks and defenses is provided.
- A Survey on Text Classification: From Traditional to Deep Learning580
A taxonomy for text classification according to the text involved and the models used for feature extraction and classification is created, dealing with both the technical developments and benchmark datasets that support tests of predictions.
- A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT554
This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.
- A generalist vision–language foundation model for diverse biomedical tasks469
BiomedGPT is described, the first open-source and lightweight vision–language foundation model, designed as a generalist capable of performing various biomedical tasks, which achieves state-of-the-art accuracy in 16 out of 25 biomedical tasks, with promising performance in a series of potential clinical applications.
- Adversarial Attack and Defense on Graph Data: A Survey409
This review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.
- Decentralized Federated Learning: A Survey and Perspective362
A systematic and detailed perspective on DFL, including iteration order, communication protocols, network topologies, paradigm proposals, and temporal variability, and several extended variants and categorizations are proposed with state-of-the-art (SOTA) technologies.
- User Preference-aware Fake News Detection357
A new framework, UPFD, is proposed, which simultaneously captures various signals from user preferences by joint content and graph modeling, and demonstrates the effectiveness of the proposed framework on real-world datasets.
- AugGPT: Leveraging ChatGPT for Text Data Augmentation316
Experimental results on multiple few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.
- LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy282
A novel design of local differential privacy mechanism for federated learning that makes the local weights update differentially private by adapting to the varying ranges at different layers of a deep neural network, which introduces a smaller variance of the estimated model weights, especially for deeper models.
- MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use244
MetaTool is introduced, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools, and recommends that tool developers choose an appropriate rewrite model for generating new descriptions based on the downstream LLM the tool will apply to.
- Optimization-based Prompt Injection Attack to LLM-as-a-Judge237
This work proposes JudgeDeceiver, an optimization-based prompt injection attack to LLM-as-a-Judge that injects a carefully crafted sequence into an attacker-controlled candidate response such that LLM-as-a-Judge selects the candidate response for an attacker-chosen question no matter what other candidate responses are.
- MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation235
A modality-agnostic SAM adaptation framework that is applicable to various volumetric and video medical data is introduced, and without using any prompt, consistently outperforms various state-of-the-art 3D approaches.
- A Survey of AI-Generated Content (AIGC)220
This survey provides a comprehensive review of the history of generative models and recent advances in AIGC, focusing on both unimodal and multimodal interaction.
- FedBERT : When Federated Learning Meets Pre-training182
This work proposes a new learning approach, FedBERT, that takes advantage of the federated learning and split learning approaches, resorting to pre-training BERT in a federated way and can prevent sharing the raw data information and obtain excellent performance.
- End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models172
The first to build the benchmark dataset and solutions for end-to-end multimodal fact-checking and explanation generation and to establish baseline performances on MOCHEG, it is demonstrated that the performance of the state-of-the-art end- to-end multi-million-dollar fact- checking does not provide satisfactory outcomes.
- Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks156
The studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.
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- Federated Model Distillation with Noise-Free Differential Privacy135
This paper proposes a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework, and demonstrates the feasibility of this framework by considering both IID and Non-IID settings, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
- Private Model Compression via Knowledge Distillation133
A private model compression framework RONA, which jointly uses hint learning, distillation learning, and self learning to train a compact and fast neural network, which can not only compress cumbersome models efficiently but also provide a strong privacy guarantee.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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