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- Federated Graph Neural Networks: Overview, Techniques, and Challenges205
A 2-D taxonomy of the FedGNN literature is proposed: the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNN training and the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients.
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- Approximation Algorithms for Product Framing and Pricing84
One of the first models of “product framing” and pricing and pricing is proposed, based on approximation algorithms for product framing and pricing.
- Slideflow: deep learning for digital histopathology with real-time whole-slide visualization77
A flexible deep learning library for histopathology called Slideflow is developed, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models.
- Advances and Open Challenges in Federated Foundation Models75
A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization, and offering a blueprint for future research directions, emphasizing the need for developing trustworthy solutions.
- FedMut: Generalized Federated Learning via Stochastic Mutation60
A novel FL approach is presented, which mutates the global model according to the gradient change to generate several intermediate models for the next round of training, which has a well-generalization compared with the global model trained by FedAvg.
- FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation59
Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, Fed Cross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead.
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- FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning50
The Federated Stochastic Dual-Gate based Feature Selection (FedSDG-FS) approach consists of a Gaussian stochastic dual-gate to efficiently approximate the probability of a feature being selected, with privacy protection through Partially Homomorphic Encryption without a trusted third-party.
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- VulAdvisor: Natural Language Suggestion Generation for Software Vulnerability Repair31
VulAdvisor is introduced, an automated approach that generates natural language suggestions to guide developers or AVR tools in repairing vulnerabilities and it is shown that the generated suggestions enhance the patch generation capabilities of existing AVR tools.
- Ciron : a new benchmark dataset for Chinese irony detection25
Ciron is introduced, the first Chinese benchmark dataset available for irony detection for machine learning models, and is collected with no pre-conditions to ensure a much wider coverage.
- FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios24
A novel heterogeneous FL framework named FlexFL, based on the self-knowledge distillation, which can enhance the inference performance of the large models by learning the knowledge from the small models, can significantly improve the overall inference accuracy.
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- FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data19
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- RF-Eye: Commodity RFID Can Know What You Write and Who You Are Wherever You Are17
RF-Eye is introduced, a system that enables contactless, position-independent handwriting recognition and user identification without prior training and proposes a novel feature, DCG, to capture changes in gesture direction and confirm its consistency across different positions.
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- Learning Program Representations with a Tree-Structured Transformer14
The Tree-Transformer is proposed, a novel recursive tree-structured neural network to learn the vector representations for source codes that significantly outperforms the existing tree-based and graph-based program representation learning approaches in both the tree-level and node-level prediction tasks.
- FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning14
The Federated Client and Sample Selection (FedCSS) approach is a bilevel optimization approach for FL client-and-sample selection to achieve hard sample-aware noise-robust learning in a privacy preserving manner.
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 2026-10-12. 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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