Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quality-insensitive perception bias inevitably leads to a heavy reliance on image semantics when those LMMs are forced for quality rating. In this paper, instead of retraining or tuning an LMM costly, we propose a training-free debiasing framework, in which the image quality prediction is rectified by mitigating the bias caused by image semantics. Specifically, we first explore several semantic-preserving distortions that can significantly degrade image quality while maintaining identifiable semantics. By applying these specific distortions to the query/test images, we ensure that the degraded images are recognized as poor quality while their semantics remain. During quality inference, both a query image and its corresponding degraded version are fed to the LMM along with a prompt indicating that the query image quality should be inferred under the condition that the degraded one is deemed poor quality. This prior condition effectively aligns the LMM’s quality perception, as all degraded images are consistently rated as poor quality, regardless of their semantic difference. Finally, the quality scores of the query image inferred under different prior conditions (degraded versions) are aggregated using a conditional probability model. Extensive experiments on various IQA datasets show that our debiasing framework could consistently enhance the LMM performance and the code will be publicly available.

Personalized federated learning (PFL) tailors models to clients' unique data distributions while preserving privacy. However, existing aggregation-weight-based PFL methods often struggle with heterogeneous data, facing challenges in accuracy, computational efficiency, and communication overhead. We propose FedAPA, a novel PFL method featuring a server-side, gradient-based adaptive aggregation strategy to generate personalized models, by updating aggregation weights based on gradients of client-parameter changes with respect to the aggregation weights in a centralized manner. FedAPA guarantees theoretical convergence and achieves superior accuracy and computational efficiency compared to 10 PFL competitors across three datasets, with competitive communication overhead. The code and full proofs are available at: https://github.com/Yuxia-Sun/FL_FedAPA.

Mobile Edge Computing (MEC) facilitates computing and storage at edge nodes near user devices, reducing latency and optimizing bandwidth. Federated Learning (FL) complements MEC by enabling privacy-preserving collaborative model training across edge nodes without sharing raw data. However, in MEC environments, FL faces challenges such as communication inefficiency and data heterogeneity (Non-IID), which degrade model performance and hinder convergence. To address these issues, we propose FedLFP, a communication-efficient personalized federated learning approach using label-free prototypes for Non-IID data in MEC. FedLFP employs three key strategies: (1) a Label-Free Prototype strategy to reduce communication costs and mitigate privacy risks, (2) a centroid prototype and combined clustering weight strategy to improve global prototype quality by considering data quantity and confidence levels, and (3) a multifaceted weighted contrastive learning strategy to enhance local representation learning and global alignment. We evaluated FedLFP on Android malware recognition using the KronoDroid dataset and standard image classification tasks, with eight configurations representing practical Non-IID settings. Experimental results show that FedLFP consistently outperforms thirteen state-of-the-art FL methods in accuracy, communication and computational efficiency. Additionally, we provide theoretical guarantees for the convergence of FedLFP under Non-IID conditions.