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TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training
Guanjie Cheng, Boyi Li, Lingyu Sun +4
Large-scale multimodal pre-trained models like CLIP rely heavily on high-quality training data, yet raw web-crawled datasets are often noisy, misaligned, and redundant, leading to…
Shiva-DiT: Residual-Based Differentiable Top- Selection for Efficient Diffusion Transformers
Jiaji Zhang, Hailiang Zhao, Jiaju Wu +3
Diffusion Transformers (DiTs) are costly at high resolution because self-attention scales quadratically with token sequence length. Existing pruning methods do not jointly provide…
Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection
Zhiwei Ling, Hailiang Zhao, Chao Zhang +8
Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent serv…
LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping
Guanjie Cheng, Mengzhen Yang, Xinkui Zhao +5
Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient…
ALPINE: Closed-Loop Adaptive Privacy Budget Allocation for Mobile Edge Crowdsensing
Guanjie Cheng, Siyang Liu, Xinkui Zhao +4
Mobile edge crowdsensing (MECS) enables large-scale real-time sensing services, but its continuous data collection and transmission pipeline exposes terminal devices to dynamic pri…
Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation
Wenzhuo Qian, Hailiang Zhao, Jiayi Chen +7
Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the maintenance of strict Service Level Objective…