28 research outputs found
RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System
Federated Learning (FL) is an emerging decentralized artificial intelligence
paradigm, which promises to train a shared global model in high-quality while
protecting user data privacy. However, the current systems rely heavily on a
strong assumption: all clients have a wealth of ground truth labeled data,
which may not be always feasible in the real life. In this paper, we present a
practical Robust, and Communication-efficient Semi-supervised FL (RC-SSFL)
system design that can enable the clients to jointly learn a high-quality model
that is comparable to typical FL's performance. In this setting, we assume that
the client has only unlabeled data and the server has a limited amount of
labeled data. Besides, we consider malicious clients can launch poisoning
attacks to harm the performance of the global model. To solve this issue,
RC-SSFL employs a minimax optimization-based client selection strategy to
select the clients who hold high-quality updates and uses geometric median
aggregation to robustly aggregate model updates. Furthermore, RC-SSFL
implements a novel symmetric quantization method to greatly improve
communication efficiency. Extensive case studies on two real-world datasets
demonstrate that RC-SSFL can maintain the performance comparable to typical FL
in the presence of poisoning attacks and reduce communication overhead by
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Recent years have witnessed the dramatic growth of paper volumes with plenty
of new research papers published every day, especially in the area of computer
science. How to glean papers worth reading from the massive literature to do a
quick survey or keep up with the latest advancement about a specific research
topic has become a challenging task. Existing academic search engines such as
Google Scholar return relevant papers by individually calculating the relevance
between each paper and query. However, such systems usually omit the
prerequisite chains of a research topic and cannot form a meaningful reading
path. In this paper, we introduce a new task named Reading Path Generation
(RPG) which aims at automatically producing a path of papers to read for a
given query. To serve as a research benchmark, we further propose SurveyBank, a
dataset consisting of large quantities of survey papers in the field of
computer science as well as their citation relationships. Each survey paper
contains key phrases extracted from its title and multi-level reading lists
inferred from its references. Furthermore, we propose a
graph-optimization-based approach for reading path generation which takes the
relationship between papers into account. Extensive evaluations demonstrate
that our approach outperforms other baselines. A Real-time Reading Path
Generation System (RePaGer) has been also implemented with our designed model.
To the best of our knowledge, we are the first to target this important
research problem. Our source code of RePaGer system and SurveyBank dataset can
be found on here.Comment: 16 pages, 12 figure
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer
With the rapid development of cloud manufacturing, industrial production with
edge computing as the core architecture has been greatly developed. However,
edge devices often suffer from abnormalities and failures in industrial
production. Therefore, detecting these abnormal situations timely and
accurately is crucial for cloud manufacturing. As such, a straightforward
solution is that the edge device uploads the data to the cloud for anomaly
detection. However, Industry 4.0 puts forward higher requirements for data
privacy and security so that it is unrealistic to upload data from edge devices
directly to the cloud. Considering the above-mentioned severe challenges, this
paper customizes a weakly-supervised edge computing anomaly detection
framework, i.e., Federated Learning-based Transformer framework
(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud
manufacturing. Specifically, we introduce federated learning (FL) framework
that allows edge devices to train an anomaly detection model in collaboration
with the cloud without compromising privacy. To boost the privacy performance
of the framework, we add differential privacy noise to the uploaded features.
To further improve the ability of edge devices to extract abnormal features, we
use the Transformer to extract the feature representation of abnormal data. In
this context, we design a novel collaborative learning protocol to promote
efficient collaboration between FL and Transformer. Furthermore, extensive case
studies on four benchmark data sets verify the effectiveness of the proposed
framework. To the best of our knowledge, this is the first time integrating FL
and Transformer to deal with anomaly detection problems in cloud manufacturing
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Providing Emotional Support (ES) to soothe people in emotional distress is an
essential capability in social interactions. Most existing researches on
building ES conversation systems only considered single-turn interactions with
users, which was over-simplified. In comparison, multi-turn ES conversation
systems can provide ES more effectively, but face several new technical
challenges, including: (1) how to adopt appropriate support strategies to
achieve the long-term dialogue goal of comforting the user's emotion; (2) how
to dynamically model the user's state. In this paper, we propose a novel system
MultiESC to address these issues. For strategy planning, drawing inspiration
from the A* search algorithm, we propose lookahead heuristics to estimate the
future user feedback after using particular strategies, which helps to select
strategies that can lead to the best long-term effects. For user state
modeling, MultiESC focuses on capturing users' subtle emotional expressions and
understanding their emotion causes. Extensive experiments show that MultiESC
significantly outperforms competitive baselines in both dialogue generation and
strategy planning. Our codes are available at
https://github.com/lwgkzl/MultiESC.Comment: Accepted by the main conference of EMNLP 202
The associations between gut microbiota and chronic respiratory diseases: a Mendelian randomization study
IntroductionGrowing evidence indicates that variations in the composition of the gut microbiota are linked to the onset and progression of chronic respiratory diseases (CRDs), albeit the causal relationship between the two remains unclear.MethodsWe conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the relationship between gut microbiota and five main CRDs, including chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sarcoidosis, and pneumoconiosis. For MR analysis, the inverse variance weighted (IVW) method was utilized as the primary method. The MR–Egger, weighted median, and MR-PRESSO statistical methods were used as a supplement. To detect heterogeneity and pleiotropy, the Cochrane and Rucker Q test, MR–Egger intercept test, and MR-PRESSO global test were then implemented. The leave-one-out strategy was also applied to assess the consistency of the MR results.ResultsBased on substantial genetic data obtained from genome-wide association studies (GWAS) comprising 3,504,473 European participants, our study offers evidence that several gut microbial taxa, including 14 probable microbial taxa (specifically, 5, 3, 2, 3 and 1 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) and 33 possible microbial taxa (specifically, 6, 7, 8, 7 and 5 for COPD, asthma, IPF, sarcoidosis, and pneumoconiosis, respectively) play significant roles in the formation of CRDs.DiscussionThis work implies causal relationships between the gut microbiota and CRDs, thereby shedding new light on the gut microbiota-mediated prevention of CRDs