10,376 research outputs found
-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI
In the wake of the burgeoning expansion of generative artificial intelligence
(AI) services, the computational demands inherent to these technologies
frequently necessitate cloud-powered computational offloading, particularly for
resource-constrained mobile devices. These services commonly employ prompts to
steer the generative process, and both the prompts and the resultant content,
such as text and images, may harbor privacy-sensitive or confidential
information, thereby elevating security and privacy risks. To mitigate these
concerns, we introduce -Split, a split computing framework to
facilitate computational offloading while simultaneously fortifying data
privacy against risks such as eavesdropping and unauthorized access. In
-Split, a generative model, usually a deep neural network (DNN), is
partitioned into three sub-models and distributed across the user's local
device and a cloud server: the input-side and output-side sub-models are
allocated to the local, while the intermediate, computationally-intensive
sub-model resides on the cloud server. This architecture ensures that only the
hidden layer outputs are transmitted, thereby preventing the external
transmission of privacy-sensitive raw input and output data. Given the
black-box nature of DNNs, estimating the original input or output from
intercepted hidden layer outputs poses a significant challenge for malicious
eavesdroppers. Moreover, -Split is orthogonal to traditional
encryption-based security mechanisms, offering enhanced security when deployed
in conjunction. We empirically validate the efficacy of the -Split
framework using Llama 2 and Stable Diffusion XL, representative large language
and diffusion models developed by Meta and Stability AI, respectively. Our
-Split implementation is publicly accessible at
https://github.com/nishio-laboratory/lambda_split.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Data-Centric Foundation Models in Computational Healthcare: A Survey
The advent of foundation models (FMs) as an emerging suite of AI techniques
has struck a wave of opportunities in computational healthcare. The interactive
nature of these models, guided by pre-training data and human instructions, has
ignited a data-centric AI paradigm that emphasizes better data
characterization, quality, and scale. In healthcare AI, obtaining and
processing high-quality clinical data records has been a longstanding
challenge, ranging from data quantity, annotation, patient privacy, and ethics.
In this survey, we investigate a wide range of data-centric approaches in the
FM era (from model pre-training to inference) towards improving the healthcare
workflow. We discuss key perspectives in AI security, assessment, and alignment
with human values. Finally, we offer a promising outlook of FM-based analytics
to enhance the performance of patient outcome and clinical workflow in the
evolving landscape of healthcare and medicine. We provide an up-to-date list of
healthcare-related foundation models and datasets at
https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare
Influence of social networks on communication and culture
Má bakalářská práce je zaměřena na problematiku sociálních sítí a jejich vliv na dnešní společnost. Zabývá se jejich historií od prvotních pokusů až po nejnovější globální sociání sítě. Poté práce vysvětluje základní myšlenku vedoucí k vytvoření sociálních sítí i jejich charakteristické znaky. Dále nastiňuje problémy související se snadnou dostupností a nadměrným využíváním sociálních sítí, které následně ovlivňuje lidskou společnost. Práce se věnuje vlivu sociálních sítí na jazyk, mezilidskou komunikaci a kulturní adaptaci.My bachelor thesis is focused on issues with social networking services and their influence on modern society. It addresses their history from the very first attempts to create a social networking service to the modern global ones. Later the thesis provides an explanation of the creation of a social networking service and its characteristic traits. Furthermore it outlines problems connected with the availability and overuse of social networking services that are subsequently influencing the human society. The thesis also analyzes the influence of social networks on language, interpersonal communication and cultural adaptation.
Orthogonal Adaptation for Modular Customization of Diffusion Models
Customization techniques for text-to-image models have paved the way for a
wide range of previously unattainable applications, enabling the generation of
specific concepts across diverse contexts and styles. While existing methods
facilitate high-fidelity customization for individual concepts or a limited,
pre-defined set of them, they fall short of achieving scalability, where a
single model can seamlessly render countless concepts. In this paper, we
address a new problem called Modular Customization, with the goal of
efficiently merging customized models that were fine-tuned independently for
individual concepts. This allows the merged model to jointly synthesize
concepts in one image without compromising fidelity or incurring any additional
computational costs.
To address this problem, we introduce Orthogonal Adaptation, a method
designed to encourage the customized models, which do not have access to each
other during fine-tuning, to have orthogonal residual weights. This ensures
that during inference time, the customized models can be summed with minimal
interference.
Our proposed method is both simple and versatile, applicable to nearly all
optimizable weights in the model architecture. Through an extensive set of
quantitative and qualitative evaluations, our method consistently outperforms
relevant baselines in terms of efficiency and identity preservation,
demonstrating a significant leap toward scalable customization of diffusion
models.Comment: Project page: https://ryanpo.com/ortha
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and
utilization of data to drive AI models and applications, has attracted
increasing attention in recent years. In this article, we conduct an in-depth
and comprehensive review, offering a forward-looking outlook on the current
efforts in data-centric AI pertaining to graph data-the fundamental data
structure for representing and capturing intricate dependencies among massive
and diverse real-life entities. We introduce a systematic framework,
Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of
the graph data lifecycle, including graph data collection, exploration,
improvement, exploitation, and maintenance. A thorough taxonomy of each stage
is presented to answer three critical graph-centric questions: (1) how to
enhance graph data availability and quality; (2) how to learn from graph data
with limited-availability and low-quality; (3) how to build graph MLOps systems
from the graph data-centric view. Lastly, we pinpoint the future prospects of
the DC-GML domain, providing insights to navigate its advancements and
applications.Comment: 42 pages, 9 figure
- …