Selected work

Representative Papers

Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation

Aug 11, 2026

This work addresses the limitations of existing personalized federated reinforcement learning methods, which overly rely on extrinsic rewards and suffer from insufficient exploration in non-stationary or sparse-reward environments, leading to weak policy personalization and low sample efficiency. To overcome these challenges, we propose the first exploration-driven personalized federated reinforcement learning framework that integrates intrinsic motivation. Specifically, clients leverage Random Network Distillation (RND) to generate intrinsic rewards that enhance local exploration, while the server aggregates only minimal novelty summaries and broadcasts a global exploration prior to coordinate diverse cross-client exploration without compromising privacy. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods on standard benchmarks as well as in sparse and delayed reward settings, achieving both stronger policy personalization and higher sample efficiency.

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UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging

Jul 23, 2026

This work addresses the challenge of cross-modal knowledge transfer in medical imaging under the practical constraint of unpaired multi-modal data. The authors propose UnDA, a novel framework that enables robust knowledge distillation and domain alignment without requiring paired samples. By leveraging anchor-guided extraction of semantic, structured class tokens, the method integrates attention pooling, an uncertainty-weighted optimal transport mechanism (UCT-OT) based on prediction confidence, and prototype-based contrastive learning (ProtoNCE) to dynamically suppress noisy predictions from the source domain and enhance inter-class consistency. Key innovations include a backbone-agnostic alignment module, the confidence-aware UCT-OT strategy, and a memory bank for maintaining class prototypes. Extensive experiments demonstrate that UnDA significantly improves segmentation accuracy and boundary precision on the target modality under strict unpaired settings, validating its effectiveness in transferring structured knowledge across heterogeneous imaging modalities.

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Balancing Accuracy and Efficiency: CNN Fusion Models for Diabetic Retinopathy Screening

Dec 25, 2025

To address the challenge of balancing accuracy and efficiency in global diabetic retinopathy (DR) screening—exacerbated by heterogeneous fundus image quality and scarcity of ophthalmologic expertise—this paper proposes a lightweight cross-device feature-fusion CNN. We systematically evaluate the generalization benefits of feature-level fusion between EfficientNet-B0 and DenseNet121, extracting features via pretrained ResNet50, EfficientNet-B0, and DenseNet121; features are concatenated and jointly trained across five diverse datasets, with rigorous five-fold independent evaluation. Our proposed Eff+Den model achieves 82.89% overall accuracy on multi-source, heterogeneous data (F1-scores: 83.60% for normal, 82.60% for pathological cases), with only 1.42 ms inference latency per image. It significantly outperforms individual models and three-model fusion baselines, achieving a favorable trade-off among high accuracy, strong cross-dataset generalization, and ultra-low latency—enabling deployable, resource-efficient DR triage in low-resource settings.

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Recent publications

Latest Papers

Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation

Aug 11, 2026

This work addresses the limitations of existing personalized federated reinforcement learning methods, which overly rely on extrinsic rewards and suffer from insufficient exploration in non-stationary or sparse-reward environments, leading to weak policy personalization and low sample efficiency. To overcome these challenges, we propose the first exploration-driven personalized federated reinforcement learning framework that integrates intrinsic motivation. Specifically, clients leverage Random Network Distillation (RND) to generate intrinsic rewards that enhance local exploration, while the server aggregates only minimal novelty summaries and broadcasts a global exploration prior to coordinate diverse cross-client exploration without compromising privacy. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods on standard benchmarks as well as in sparse and delayed reward settings, achieving both stronger policy personalization and higher sample efficiency.

0 citationsRead paper

UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging

Jul 23, 2026

This work addresses the challenge of cross-modal knowledge transfer in medical imaging under the practical constraint of unpaired multi-modal data. The authors propose UnDA, a novel framework that enables robust knowledge distillation and domain alignment without requiring paired samples. By leveraging anchor-guided extraction of semantic, structured class tokens, the method integrates attention pooling, an uncertainty-weighted optimal transport mechanism (UCT-OT) based on prediction confidence, and prototype-based contrastive learning (ProtoNCE) to dynamically suppress noisy predictions from the source domain and enhance inter-class consistency. Key innovations include a backbone-agnostic alignment module, the confidence-aware UCT-OT strategy, and a memory bank for maintaining class prototypes. Extensive experiments demonstrate that UnDA significantly improves segmentation accuracy and boundary precision on the target modality under strict unpaired settings, validating its effectiveness in transferring structured knowledge across heterogeneous imaging modalities.

0 citationsRead paper

Balancing Accuracy and Efficiency: CNN Fusion Models for Diabetic Retinopathy Screening

Dec 25, 2025

To address the challenge of balancing accuracy and efficiency in global diabetic retinopathy (DR) screening—exacerbated by heterogeneous fundus image quality and scarcity of ophthalmologic expertise—this paper proposes a lightweight cross-device feature-fusion CNN. We systematically evaluate the generalization benefits of feature-level fusion between EfficientNet-B0 and DenseNet121, extracting features via pretrained ResNet50, EfficientNet-B0, and DenseNet121; features are concatenated and jointly trained across five diverse datasets, with rigorous five-fold independent evaluation. Our proposed Eff+Den model achieves 82.89% overall accuracy on multi-source, heterogeneous data (F1-scores: 83.60% for normal, 82.60% for pathological cases), with only 1.42 ms inference latency per image. It significantly outperforms individual models and three-model fusion baselines, achieving a favorable trade-off among high accuracy, strong cross-dataset generalization, and ultra-low latency—enabling deployable, resource-efficient DR triage in low-resource settings.

0 citationsRead paper