Reducing Communication for Split Learning by Randomized Top-k Sparsification

Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model generalize and converge better. This is done by selecting top-k elements with a large probability while also having a small probability to select non-top-k elements. Empirical results show that compared with other communication-reduction methods, our proposed randomized top-k sparsification achieves a better model performance under the same compression level.Comment: Accepted by IJCAI 202

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. Images determined as overall dissimilar, on the other hand, indicate higher robustness against attack. However, there is no guarantee that these metrics well reflect human opinions, which, as a judgement for model privacy leakage, are more trustworthy. In this paper, we comprehensively study the faithfulness of these hand-crafted metrics to human perception of privacy information from the reconstructed images. On 5 datasets ranging from natural images, faces, to fine-grained classes, we use 4 existing attack methods to reconstruct images from many different classification models and, for each reconstructed image, we ask multiple human annotators to assess whether this image is recognizable. Our studies reveal that the hand-crafted metrics only have a weak correlation with the human evaluation of privacy leakage and that even these metrics themselves often contradict each other. These observations suggest risks of current metrics in the community. To address this potential risk, we propose a learning-based measure called SemSim to evaluate the Semantic Similarity between the original and reconstructed images. SemSim is trained with a standard triplet loss, using an original image as an anchor, one of its recognizable reconstructed images as a positive sample, and an unrecognizable one as a negative. By training on human annotations, SemSim exhibits a greater reflection of privacy leakage on the semantic level. We show that SemSim has a significantly higher correlation with human judgment compared with existing metrics. Moreover, this strong correlation generalizes to unseen datasets, models and attack methods.Comment: 15 pages, 9 figures and 3 table

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach

Reduced Annexin A1 Secretion by ABCA1 Causes Retinal Inflammation and Ganglion Cell Apoptosis in a Murine Glaucoma Model

Variants near the ATP-binding cassette transporter A1 (ABCA1) gene are associated with elevated intraocular pressure and newly discovered risk factors for glaucoma. Previous studies have shown an association between ABCA1 deficiency and retinal inflammation. Using a mouse model of ischemia-reperfusion (IR) induced by acute intraocular pressure elevation, we found that the retinal expression of ABCA1 protein was decreased. An induction of ABCA1 expression by liver X receptor agonist TO901317 reduced retinal ganglion cell (RGC) apoptosis after IR and promoted membrane translocation and secretion of the anti-inflammatory factor annexin A1 (ANXA1). Moreover, ABCA1 and ANXA1 co-localized in cell membranes, and the interaction domain is amino acid 196 to 274 of ANXA1 fragment. TO901317 also reduced microglia migration and activation and decreased the expression of pro-inflammatory cytokines interleukin (IL)-17A and IL-1β, which could be reversed by the ANXA1 receptor blocker Boc2. Overexpression of TANK-binding kinase 1 (TBK1) increased ABCA1 degradation, which was reversed by the proteasome inhibitor carbobenzoxy-L-leucyl-L-leucyl-L-leucinal (MG132). Silencing Tbk1 with siRNA increased ABCA1 expression and promoted ANXA1 membrane translocation. These results indicate a novel IR mechanism, that leads via TBK1 activation to ABCA1 ubiquitination. This degradation decreases ANXA1 secretion, thus facilitating retinal inflammation and RGC apoptosis. Our findings suggest a potential treatment strategy to prevent RGC apoptosis in retinal ischemia and glaucoma

The influence of sleep factors and dietary habits on the disease pattern of ulcerative colitis patients with long and short disease courses – a multicentre cross-sectional analysis

Ulcerative colitis (UC) is a disease characterized by chronic relapsing-remitting inflammatory disorders and is associated with environmental changes. To explore the disease patterns of Chinese UC patients and to determine controllable related environmental factors. This multicentre cross-sectional study was performed using a questionnaire survey. Data on clinical characteristics and environmental factors were collected. Patients with a disease course ≥5 years were defined as the long course group, and those with a disease course A total of 588 effective questionnaires were collected. The proportion of the chronic continuous pattern was the highest among patients with a long disease course (46.8%), and in patients with a short disease course, the proportion of the active to remission pattern was the highest (53.3%). In patients with a long disease course, a higher proportion of patients with adequate sleep was found in the active to remission pattern than in the chronic intermittent (72.1% vs. 43.3%, p = 0.008) and chronic continuous (72.1% vs. 52.4%, p = 0.016) patterns. In patients with a short disease course, the frequency of shellfish and shrimp was higher in the chronic continuous pattern group than in the active to remission pattern group (P = 0.001 and 0.017 respectively). For early diagnosis patients, dietary guidance should be actively carried out. With the prolongation of the disease course, attention should be given to the sleep quality of patients. 1.UC exhibits various disease patterns, which may be associated with differences in patient prognosis and treatment response. 2.Environmental factors, especially sleep and dietary factors, correlated strongly with disease patterns, which varied in different disease courses. 3.Early diagnosis patients should receive active dietary guidance, while patients with a prolonged disease course require attention to their sleep quality and appropriate drug interventions when necessary.</p