Reducing Communication for Split Learning by Randomized Top-k Sparsification
* External authors
The EU AI Act proposal addresses, among other applications, AI systems that enable facial classification and emotion recognition. As part of previous work, we have investigated how citizens deliberate about the validity of AI-based facial classifications in the advertisement and the hiring contexts (N= 3745). In our current research, we extend this investigation by collecting laypeople’s ethical evaluations of facial analysis AI in Japan, Argentina, Kenya and the United States (N~ 4000). Our project serves as a motivation to ask how such cross-cultural AI ethics perspectives can inform EU policymaking regarding AI systems, which enable facial classification and emotion recognition. We refer to suggestions on achieving policy impact and aim to discuss this topic space with workshop participants.
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Image…
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
With growing concerns regarding privacy in machine learning models, regulations have committed to granting individuals the right to be forgotten while mandating companies to develop non-discriminatory machine learning systems, thereby fueling the study of the machine unlearn…
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneo…