Competitive Advantage Attacks to Decentralized Federated Learning

Decentralized federated learning (DFL) enables clients (e.g., hospitals and banks) to jointly train machine learning models without a central orchestration server. In each global training round, each client trains a local model on its own training data and then they exchange local models for aggregation. In this work, we propose SelfishAttack, a new family of attacks to DFL. In SelfishAttack, a set of selfish clients aim to achieve competitive advantages over the remaining non-selfish ones, i.e., the final learnt local models of the selfish clients are more accurate than those of the non-selfish ones. Towards this goal, the selfish clients send carefully crafted local models to each remaining non-selfish one in each global training round. We formulate finding such local models as an optimization problem and propose methods to solve it when DFL uses different aggregation rules. Theoretically, we show that our methods find the optimal solutions to the optimization problem. Empirically, we show that SelfishAttack successfully increases the accuracy gap (i.e., competitive advantage) between the final learnt local models of selfish clients and those of non-selfish ones. Moreover, SelfishAttack achieves larger accuracy gaps than poisoning attacks when extended to increase competitive advantages

Prompt Injection Attacks and Defenses in LLM-Integrated Applications

Large Language Models (LLMs) are increasingly deployed as the backend for a variety of real-world applications called LLM-Integrated Applications. Multiple recent works showed that LLM-Integrated Applications are vulnerable to prompt injection attacks, in which an attacker injects malicious instruction/data into the input of those applications such that they produce results as the attacker desires. However, existing works are limited to case studies. As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses. We aim to bridge the gap in this work. In particular, we propose a general framework to formalize prompt injection attacks. Existing attacks, which are discussed in research papers and blog posts, are special cases in our framework. Our framework enables us to design a new attack by combining existing attacks. Moreover, we also propose a framework to systematize defenses against prompt injection attacks. Using our frameworks, we conduct a systematic evaluation on prompt injection attacks and their defenses with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in this field. Our code is available at https://github.com/liu00222/Open-Prompt-Injection

An Unified Search and Recommendation Foundation Model for Cold-Start Scenario

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.Comment: CIKM 2023,6 page

Heat shock transcription factor 1 preserves cardiac angiogenesis and adaptation during pressure overload

To examine how heat shock transcription factor 1 (HSF1) protects against maladaptive hypertrophy during pressure overload, we subjected HSF1 transgenic (TG), knockout (KO) and wild type (WT) mice to a constriction of transverse aorta (TAC), and found that cardiac hypertrophy, functions and angiogenesis were well preserved in TG mice but were decreased in KO mice compared to WT ones at 4 weeks, which was related to HIF-1 and p53 expression. Inhibition of angiogenesis suppressed cardiac adaptation in TG mice while overexpression of angiogenesis factors improved maladaptive hypertrophy in KO mice. In vitro formation of vasculatures by microvascular endothelial cells was higher in TG mice but lower in KO mice than in WT ones. A siRNA of p53 but not a HIF-1 gene significantly reversed maladaptive hypertrophy in KO mice whereas a siRNA of HIF-1 but not a p53 gene induced maladaptive hypertrophy in TG mice. Heart microRNA analysis showed that miR-378 and miR-379 were differently changed among the three mice after TAC, and miR-378 or siRNA of miR-379 could maintain cardiac adaptation in WT mice. These results indicate that HSF1 preserves cardiac adaptation during pressure overload through p53-HIF-1-associated angiogenesis, which is controlled by miR-378 and miR-379

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

Intracerebral hemorrhage (ICH) is the second most common and deadliest form of stroke. Despite medical advances, predicting treat ment outcomes for ICH remains a challenge. This paper proposes a novel prognostic model that utilizes both imaging and tabular data to predict treatment outcome for ICH. Our model is trained on observational data collected from non-randomized controlled trials, providing reliable predictions of treatment success. Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials. Importantly, we develop a variational distributions combination module that combines the information from imaging data, non-imaging clinical data, and treatment assignment to accurately generate the prognostic score. We conducted extensive experiments on a real-world clinical dataset of intracerebral hemorrhage. Our proposed method demonstrates a substantial improvement in treatment outcome prediction compared to existing state-of-the-art approaches. Code is available at https://github.com/med-air/TOP-GP

Does sacubitril/valsartan work in children with heart failure?—a pilot study

BackgroundSacubitril/valsartan is an angiotensin receptor neprilysin antagonist (ARNI) approved for adult heart failure (HF). Its safety and efficacy in pediatric HF patients with cardiomyopathy or congenital heart disease are poorly understood. A pilot study was conducted to assess the clinical response, efficacy and safety of sacubitril/valsartan in this population at a tertiary care hospital in China.MethodsClinical parameters of patients who received sacubitril/valsartan from January 2019 to March 2023 were retrospectively collected and analyzed. Children over 1 month with a left ventricular ejection fraction (LVEF) <45% were included. Clinical efficacy was evaluated by echocardiographic LVEF, N-terminal pro-brain natriuretic peptide (NT-proBNP), New York Heart Association (NYHA) HF classification, HF re-admission, and death or transplantation. The initial dose was either 0.2 mg/kg bid or 0.4 mg/kg bid, with a target dose of 2.3 mg/kg bid or 3.1 mg/kg bid.ResultsForty-five patients (60% male) with a median age of 7.86 years were enrolled. Among them, 23 had congenital heart disease and 22 had cardiomyopathies. The median maintenance dose was 0.76 mg/kg. The primary endpoint of LVEF up to 45% was reached by 24 patients (53.3%). The median NT-proBNP was significantly decreased from 5,501.5 pg/ml to 2,241.5 pg/ml (P < 0.001), more in congenital heart disease than in cardiomyopathies (P = 0.032). The NYHA HF class was improved or remained stable in 42 cases (93.3%). During a median follow-up of 1.23 years, 13 patients (28.9%) were re-hospitalized due to HF, and 9 patients (20%) died or underwent transplantation. Hypotension was the main adverse event, occurring in 8 patients.ConclusionsSacubitril/valsartan may be effective in children with HF, but its safety and outcomes may differ depending on the etiology and anatomy of HF. Early post-operative congenital heart disease patients had less tolerance, more hypotension but better recovery and outcomes, while mid- and late- post-operative congenital heart disease patients and cardiomyopathy patients had less side effects but poorer clinical outcomes

Postoperative hypothalamic-pituitary dysfunction and long-term hormone replacement in patients with childhood-onset craniopharyngioma

ObjectiveHypothalamic-pituitary axis dysfunction is a common complication in post-operative craniopharyngioma(CP) patients, and it greatly impacts the long-term quality of life of such patients. To better understand the effects of postoperative hypothalamic-pituitary dysfunction and long-term hormone replacement therapy in patients with childhood CP, we assessed approximately 200 patients with childhood-onset CP postoperatively.MethodsClinical details of patients with childhood-onset CP who underwent sellar tumor resection in Beijing Children’s Hospital and Beijing Tiantan Hospital from 2018 to 2019 were retrieved retrospectively. The participants were followed up to assess the effects of post-operative long-term hormone replacement therapy and assess the tumor recurrence rate.ResultsThe median age of admission was 8.1 (1.8, 14.3) years. Headache (45.5%), visual impairment (39.5%), and nausea (33.0%) were the most common clinical manifestations. ACP accounted for 95% of all CP cases. The incidence of central adrenal insufficiency and central hypothyroidism within the first week after surgery was 56.2% and 70.3%, respectively. At the same time 85.5% of the patients required at least one dose of desmopressin to control urine output. Total survival and tumor recurrence rates were 98.6% and 26.1%, respectively, with a median follow-up time of 29.7 (19.0, 40.3) months. During the follow-up period, 28.1% patients met the diagnostic criteria for short stature, while 54.4% fit the criteria for obesity. In addition, 94.4% of the patients were taking at least one kind of hormone substitution, and 74.7% were taking three or more. The prevalence of levothyroxine, glucocorticoid, desmopressin, and growth hormone replacement therapy was 87.3%, 77.5%, 78.9% and 31.0%, respectively. The proportion of patients treated with the substitutive combination of levothyroxine, hydrocortisone, and desmopressin was 54.9%.ConclusionThis study is a large-sample systematic postoperative endocrine function evaluation of patients with childhood-onset CP. Due to the high prevalence of post-operative hypothalamic-pituitary dysfunction, patients with CP usually require long-term multiple hormone substitution therapy. Individualized management and accurate hormone replacement dosage for postoperative childhood-onset CP patients are of great importance