Recent studies have integrated Low-Rank Adaptation (LoRA) into federated learning to enable parameter-efficient fine-tuning of Large Foundation Models (LFMs) across data silos, supporting responsible and privacy-preserving collaboration in distributed environments. However, these studies assume that all clients share the same LFM, so each client fine-tunes LoRA on private data using identical pre-trained weights and the server directly aggregates the resulting LoRA updates for collaborative training. The ideal assumption rarely holds in real-world deployments, where clients typically possess heterogeneous base LFMs. Because LoRA updates are anchored to model-specific pre-trained weights, direct aggregation is not only invalid but often counterproductive, leading to sharply degraded collaborative performance. To address this issue, we propose HeteroFL-LoRA, a federated framework that enables LoRA fine-tuning across heterogeneous LFMs. We express LoRA updates through singular value adjustments, activating task-specific knowledge in an parameter-efficient manner while decoupling them from model-specific pre-trained weights. Then, we introduce a Singular Matrix–Guided Subspace Projection that employs singular matrices to achieve cross-subspace mapping, enabling heterogeneous clients to aggregate their LoRA updates in a unified representation space. In this way, task-specific knowledge can be effectively transferred and shared across different LFMs. Experimental results demonstrate that HeteroFL-LoRA achieves effective and robust collaboration across heterogeneous LFMs while maintaining advantages in both training and communication efficiency.
Zhuojia Wu, Qi Zhang, Xue-Rong Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
Unsupervised person re-identification (USL-ReID) typically relies on clustering to generate pseudo-labels, but significant cross-view appearance variations often cause images of the same identity to be split into different clusters. Training on such noisy pseudo-labels severely degrades the learned representations. Therefore, learning robust view-invariant features is paramount. Data augmentation provides a direct way to enhance invariance, yet its trade-offs in USL-ReID remain under-explored: weak augmentations usually preserve identity semantics but lack diversity, whereas strong augmentations provide richer appearance diversity at the cost of partially corrupting identity-consistent semantic cues. To address this challenge, we propose Invariant Representation learning with Progressive Prototype Refinement (IRPP), a unified framework that learns invariant and discriminative features from noisy pseudo-labels. IRPP consists of three synergistic components. First, an Augmented Dual-Contrastive Learning (ADCL) module performs dataset-level prototype-guided invariant learning by contrasting weakly and strongly augmented views against cluster-derived prototypes. Second, an Alignment and Uniformity Learning (AUL) module regularizes the mini-batch-level weak–strong feature geometry, leading to more stable feature distributions under data augmentation. Third, a Progressive Prototype Refinement (PPR) mechanism progressively optimizes cluster centroids into cleaner prototypes, thereby mitigating the influence of noisy pseudo-labels and further strengthening invariant representation learning. This closed-loop design enables prototype-guided contrastive learning, weak–strong regularization, and prototype refinement to mutually reinforce each other. Extensive experiments on standard USL-ReID benchmarks demonstrate that IRPP achieves state-of-the-art performance with a simple and efficient training pipeline. Code is available at https://github.com/Trangle12/IRPP
Xuan Tan, Qixian Zhang, Ding Qi et al.· IEEE Transactions on Image P...· 0 citations
Unsupervised person re-identification (ReID) aims to learn identity-discriminative representations without manual annotations, which is challenging due to noisy pseudo labels, background clutter, and large appearance variations. Recent studies have shown that exploiting fine-grained local cues is crucial for improving robustness in unsupervised ReID. In this context, random masking has emerged as a simple and annotation-free way to encourage the model to focus on informative regions. However, existing masking-based unsupervised ReID methods still suffer from two limitations: (1) Underused masked views: masked views are treated as degraded auxiliaries rather than exploited as fine-grained supervisory signals; (2) Weak cross-view alignment: feature alignment is restricted to mini-batch pairs, lacking explicit global alignment between masked and unmasked views across clusters. To address these issues, we propose the Mask-guided Asymmetric Contrastive and Semantic Alignment (ACSA) framework. Specifically, we introduce an Asymmetric Contrastive Learning (ACL) module with a dual-memory mechanism to separately encode masked and unmasked features, allowing masked views to serve as informative and discriminative supervision. In parallel, a Semantic Alignment Learning (SAL) module conducts multi-granularity distribution alignment by aligning both cluster-level prototypes and randomly sampled instance-level features, thereby preserving semantic consistency and intra-cluster diversity. Furthermore, to provide more reliable semantic anchors for SAL under noisy pseudo labels, we introduce a Progressive Refinement Module (PRM), which refines prototypes and features via exponential moving averaging for more stable semantic alignment. Extensive experiments validate the superiority of our method, even outperforming certain supervised counterparts. Code is available at https://github.com/Trangle12/ACSA
Xuan Tan, Ruijian Wei, Qixian Zhang et al.· IEEE Transactions on Image P...· 0 citations
HeteroFL-LoRA is proposed, a federated framework that enables LoRA fine-tuning across heterogeneous LFMs and introduces a Singular Matrix–Guided Subspace Projection that employs singular matrices to achieve cross-subspace mapping, enabling heterogeneous clients to aggregate their LoRA updates in a unified representation space.
Zhuojia Wu, Qi Zhang, Xuerong Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
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