Mitigating Group-Level Fairness Disparities in Federated Visual Language Models

Visual language models (VLMs) have shown remarkable capabilities in multimodal tasks but face challenges in maintaining fairness across demographic groups, particularly when deployed in federated learning (FL) environments. This paper addresses the critical issue of group fairness in federated VLMs by introducing FVL-FP, a novel framework that combines FL with fair prompt tuning techniques. We focus on mitigating demographic biases while preserving model performance through three innovative components: (1) Cross-Layer Demographic Fair Prompting (CDFP), which adjusts potentially biased embeddings through counterfactual regularization; (2) Demographic Subspace Orthogonal Projection (DSOP), which removes demographic bias in image representations by mapping fair prompt text to group subspaces; and (3) Fair-aware Prompt Fusion (FPF), which dynamically balances client contributions based on both performance and fairness metrics. Extensive evaluations across four benchmark datasets demonstrate that our approach reduces demographic disparity by an average of 45\% compared to standard FL approaches, while maintaining task performance within 6\% of state-of-the-art results. FVL-FP effectively addresses the challenges of non-IID data distributions in federated settings and introduces minimal computational overhead while providing significant fairness benefits. Our work presents a parameter-efficient solution to the critical challenge of ensuring equitable performance across demographic groups in privacy-preserving multimodal systems.

Quantifying and Analyzing Entity-level Memorization in Large Language Models

Large language models (LLMs) have been proven capable of memorizing their training data, which can be extracted through specifically designed prompts. As the scale of datasets continues to grow, privacy risks arising from memorization have attracted increasing attention. Quantifying language model memorization helps evaluate potential privacy risks. However, prior works on quantifying memorization require access to the precise original data or incur substantial computational overhead, making it difficult for applications in real-world language models. To this end, we propose a fine-grained, entity-level definition to quantify memorization with conditions and metrics closer to real-world scenarios. In addition, we also present an approach for efficiently extracting sensitive entities from autoregressive language models. We conduct extensive experiments based on the proposed, probing language models' ability to reconstruct sensitive entities under different settings. We find that language models have strong memorization at the entity level and are able to reproduce the training data even with partial leakages. The results demonstrate that LLMs not only memorize their training data but also understand associations between entities. These findings necessitate that trainers of LLMs exercise greater prudence regarding model memorization, adopting memorization mitigation techniques to preclude privacy violations.