2,065 research outputs found
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without
the sharing of raw data. However, recent attacks demonstrate that simply
maintaining data locality during training processes does not provide sufficient
privacy guarantees. Rather, we need a federated learning system capable of
preventing inference over both the messages exchanged during training and the
final trained model while ensuring the resulting model also has acceptable
predictive accuracy. Existing federated learning approaches either use secure
multiparty computation (SMC) which is vulnerable to inference or differential
privacy which can lead to low accuracy given a large number of parties with
relatively small amounts of data each. In this paper, we present an alternative
approach that utilizes both differential privacy and SMC to balance these
trade-offs. Combining differential privacy with secure multiparty computation
enables us to reduce the growth of noise injection as the number of parties
increases without sacrificing privacy while maintaining a pre-defined rate of
trust. Our system is therefore a scalable approach that protects against
inference threats and produces models with high accuracy. Additionally, our
system can be used to train a variety of machine learning models, which we
validate with experimental results on 3 different machine learning algorithms.
Our experiments demonstrate that our approach out-performs state of the art
solutions
Fab forms: customizable objects for fabrication with validity and geometry caching
We address the problem of allowing casual users to customize parametric models while maintaining their valid state as 3D-printable functional objects. We define Fab Form as any design representation that lends itself to interactive customization by a novice user, while remaining valid and manufacturable. We propose a method to achieve these Fab Form requirements for general parametric designs tagged with a general set of automated validity tests and a small number of parameters exposed to the casual user. Our solution separates Fab Form evaluation into a precomputation stage and a runtime stage. Parts of the geometry and design validity (such as manufacturability) are evaluated and stored in the precomputation stage by adaptively sampling the design space. At runtime the remainder of the evaluation is performed. This allows interactive navigation in the valid regions of the design space using an automatically generated Web user interface (UI). We evaluate our approach by converting several parametric models into corresponding Fab Forms.National Science Foundation (U.S.) (Grant 1138967
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning trains machine learning models on distributed devices by
aggregating local model updates instead of local data. However, privacy
concerns arise as the aggregated local models on the server may reveal
sensitive personal information by inversion attacks. Privacy-preserving
methods, such as homomorphic encryption (HE), then become necessary for FL
training. Despite HE's privacy advantages, its applications suffer from
impractical overheads, especially for foundation models. In this paper, we
present FedML-HE, the first practical federated learning system with efficient
HE-based secure model aggregation. FedML-HE proposes to selectively encrypt
sensitive parameters, significantly reducing both computation and communication
overheads during training while providing customizable privacy preservation.
Our optimized system demonstrates considerable overhead reduction, particularly
for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x
reduction for BERT), demonstrating the potential for scalable HE-based FL
deployment
On the `Semantics' of Differential Privacy: A Bayesian Formulation
Differential privacy is a definition of "privacy'" for algorithms that
analyze and publish information about statistical databases. It is often
claimed that differential privacy provides guarantees against adversaries with
arbitrary side information. In this paper, we provide a precise formulation of
these guarantees in terms of the inferences drawn by a Bayesian adversary. We
show that this formulation is satisfied by both "vanilla" differential privacy
as well as a relaxation known as (epsilon,delta)-differential privacy. Our
formulation follows the ideas originally due to Dwork and McSherry [Dwork
2006]. This paper is, to our knowledge, the first place such a formulation
appears explicitly. The analysis of the relaxed definition is new to this
paper, and provides some concrete guidance for setting parameters when using
(epsilon,delta)-differential privacy.Comment: Older version of this paper was titled: "A Note on Differential
Privacy: Defining Resistance to Arbitrary Side Information
Development of Open Source Software and Hardware Tool-Chains for Novel Electronics
3-D printing technologies have become widely adopted and have spurred innovation and efficiency across many markets. A large contributor to the success of 3-D printing are open source, low cost electronics. On-site circuit manufacturing, however, has not become as widely utilized as 3-D printing. This project attempts to address this problem by proposing and demonstrating an open source circuit board milling machine which is inexpensive, easily manufactured, and accurate. In three interdependent sub-projects, this thesis defines a standard method for designing open source hardware, the design of the bespoke circuit mill, and explores an application of the mill for novel circuit manufacturing.
The first sub-project develops a standardized process for designing, prototyping, and distributing open source hardware. Following these steps can help ensure success for each individual part of the project. In order to validate the procedure, a case study is explored of designing low cost parametric glass slide driers.
The second sub-project details the design and construction of a circuit prototyping machine. The open source design procedure is implemented to assure maximum effectiveness. A software interface is also designed to control and carry out processing steps on the milling machine. The mill minimizes lead time and production costs of experimental circuitry. The mill also stands as a strong open source tool that can help foster growth in distributed manufacturing of electronics for a wide array of applications.
The third and final sub-project explores a flexible and scalable power monitoring system. The electronics are designed according to the open source design procedure and are manufacturable with the circuit milling machine. The power meter can be used to monitor and log power consumption of a wide range of loads, including both AC and DC
Precision and Recall for Time Series
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences
Deep Reinforcement Learning for the Design of Structural Topologies
Advances in machine learning algorithms and increased computational efficiencies have given engineers new capabilities and tools for engineering design. The presented work investigates using deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to design 2D structural topologies. Three unique structural topology design problems are investigated to validate DRL as a practical design automation tool to produce high-performing designs in structural topology domains.
The first design problem attempts to find a gradient-free alternative to solving the compliance minimization topology optimization problem. In the proposed DRL environment, a DRL agent can sequentially remove elements from a starting solid material domain to form a topology that minimizes compliance. After each action, the agent receives feedback on its performance by evaluating how well the current topology satisfies the design objectives. The agent learned a generalized design strategy that produced topology designs with similar or better compliance minimization performance than traditional gradient-based topology optimization methods given various boundary conditions.
The second design problem reformulates mechanical metamaterial unit cell design as a DRL task. The local unit cells of mechanical metamaterials are built by sequentially adding material elements according to a cubic Bezier curve methodology. The unit cells are built such that, when tessellated, they exhibit a targeted nonlinear deformation response under uniaxial compressive or tensile loading. Using a variational autoencoder for domain dimension reduction and a surrogate model for rapid deformation response prediction, the DRL environment was built to allow the agent to rapidly build mechanical metamaterials that exhibit a diverse array of deformation responses with variable degrees of nonlinearity.
Finally, the third design problem expands on the second to train a DRL agent to design mechanical metamaterials with tailorable deformation and energy manipulation characteristics. The agent’s design performance was validated by creating metamaterials with a thermoplastic polyurethane (TPU) constitutive material that increased or decreased hysteresis while exhibiting the compressive deformation response of expanded thermoplastic polyurethane (E-TPU). These optimized designs were additively manufactured and underwent experimental cyclic compressive testing. The results showed the E-TPU and metamaterial with E-TPU target properties were well aligned, underscoring the feasibility of designing mechanical metamaterials with customizable deformation and energy manipulation responses. Finally, the agent\u27s generalized design capabilities were tested by designing multiple metamaterials with diverse desired loading deformation responses and specific hysteresis objectives. The combined success of these three design problems is critical in proving that a DRL agent can serve as a co-designer working with a human designer to achieve high-performing solutions in the domain of 2D structural topologies and is worthy of incorporation into a wide array of engineering design domains
Design algoritmico de um produto baseado em dados do consumidor
There is a growing trend of using computers creatively in order to enrich
the design process. There are three Computational Design techniques that
stand-out: Parametric Design, Generative Design and Algorithmic Design.
This dissertation intends to test the viability of using these techniques in a
context of product development. These techniques show tremendous potential for products that can be customizable by consumers, exploring the
combination of various manufacturing methods. To achieve these goals a
case study with customization potential and the ability to test algorithmic design techniques has been selected. The results originate from 2 approaches: a generative approach and an algorithmic approach, with each
having different evaluation methods. The generative approach is able to explore a solution search space and compares the generated curvatures, whilst
the algorithmic approach takes advantage of rapid prototyping principles.
The performance indicators for the case study’s conception stage using CD
techniques are very positive, but the production stage needs more research.Cada vez mais os computadores são usados de forma criativa para aprofundar o processo de design. Existem três técnicas de design computacional que merecem destaque: design paramétrico, design generativo e design algorítmico. Este trabalho tem como intuito testar a viabilidade do uso destas técnicas num contexto de desenvolvimento de produto. Estas técnicas demonstram um grande potencial para produtos que possam ser customizáveis, explorando a combinação de diferentes métodos de produção. Para isso foi selecionado um caso de estudo com potencial de customização onde seja possível testar a aplicação das técnicas de design algorítmico. Os resultados provêm de 2 abordagens: uma abordagem generativa e uma abordagem algorítmica, com cada abordagem a ter
um método de avaliação de resultados. A abordagem generativa varre um
espaço de soluções e compara as curvaturas geradas enquanto a abordagem
algorítmica aproveita os princípios de prototipagem rápida. Os indicadores
obtidos para a fase de conceção do caso de estudo usando as técnicas de CD
foram positivos, no entanto a fase da produção necessita mais investigação.Mestrado em Engenharia Mecânic
United States Air Force Additive Manufacturing Applications for Civil Engineering Tools and Jigs
Additive manufacturing is a technology taking the manufacturing revolutionizing the manufacturing industry. By creating three dimensional objects from the ground up, the technology does away with the traditional manufacturing methods used to design and create all products. This research examines the application of additive manufacturing (AM) with regards to tools and jigs in United States Air Force civil engineering (CE) operations. After testing the parts, a usability survey was conducted to determine the value of AM. The results of the overall research indicated that AM will definitely impact the daily operations of a CE unit and a clear need exists for the use of AM. Further, the research determined that AM has reached a point where the integration of AM into strategically coordinated units, along with proper education and training, can be beneficial for the CE career field. Finally, the results indicate that 3Dscanning technology will reach a point within the next 5 years where it can help foster the rapid build-up of 3D CE asset designs for printing applications. The overall results push forward the Air Forces 3D printing knowledge while providing critical information for decision makers on this up and coming technology
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