Index of Silicon Valley 2011

Presents data on the area's demographic, economic, societal, environmental, and political trends, including signs of economic recovery; ability to attract talent; and health, energy conservation, and development. Analyzes the crisis in local government

Intel TDX Demystified: A Top-Down Approach

Intel Trust Domain Extensions (TDX) is a new architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deployment of virtual machines in the Secure-Arbitration Mode (SEAM) with encrypted CPU state and memory, integrity protection, and remote attestation. TDX aims to enforce hardware-assisted isolation for virtual machines and minimize the attack surface exposed to host platforms, which are considered to be untrustworthy or adversarial in the confidential computing's new threat model. TDX can be leveraged by regulated industries or sensitive data holders to outsource their computations and data with end-to-end protection in public cloud infrastructure. This paper aims to provide a comprehensive understanding of TDX to potential adopters, domain experts, and security researchers looking to leverage the technology for their own purposes. We adopt a top-down approach, starting with high-level security principles and moving to low-level technical details of TDX. Our analysis is based on publicly available documentation and source code, offering insights from security researchers outside of Intel

Towards causal federated learning : a federated approach to learning representations using causal invariance

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-i.i.d.), and Out of Distribution (OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this work, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyse empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model. Although Federated Learning allows for participants to contribute their local data without revealing it, it faces issues in data security and in accurately paying participants for quality data contributions. In this report, we also propose an EOS Blockchain design and workflow to establish data security, a novel validation error based metric upon which we qualify gradient uploads for payment, and implement a small example of our Blockchain Causal Federated Learning model to analyze its performance with respect to robustness, privacy and fairness in incentivization.L’apprentissage fédéré est une approche émergente d’apprentissage automatique distribué préservant la confidentialité pour créer un modèle partagé en effectuant une formation distribuée localement sur les appareils participants (clients) et en agrégeant les modèles locaux en un modèle global. Comme cette approche empêche la collecte et l’agrégation de données, elle contribue à réduire dans une large mesure les risques associés à la vie privée. Cependant, les échantillons de données de tous les clients participants sont généralement pas indépendante et distribuée de manière identique (non-i.i.d.), et la généralisation hors distribution (OOD) pour les modèles appris peut être médiocre. Outre ce défi, l’apprentissage fédéré reste également vulnérable à diverses attaques contre la sécurité dans lesquelles quelques entités participantes malveillantes s’efforcent d’insérer des portes dérobées, dégradant le modèle agrégé généré ainsi que d’inférer les données détenues par les entités participantes. Dans cet article, nous proposons une approche pour l’apprentissage des caractéristiques invariantes (causales) communes à tous les clients participants dans une configuration d’apprentissage fédérée et analysons empiriquement comment elle améliore la précision hors distribution (OOD) ainsi que la confidentialité du modèle appris final. Bien que l’apprentissage fédéré permette aux participants de contribuer leurs données locales sans les révéler, il se heurte à des problèmes de sécurité des données et de paiement précis des participants pour des contributions de données de qualité. Dans ce rapport, nous proposons également une conception et un flux de travail EOS Blockchain pour établir la sécurité des données, une nouvelle métrique basée sur les erreurs de validation sur laquelle nous qualifions les téléchargements de gradient pour le paiement, et implémentons un petit exemple de notre modèle d’apprentissage fédéré blockchain pour analyser ses performances

San Juan Bautista General Plan Update: Background Report, Fall 2014

This Background Report is an integral part of the San Juan Bautista 2035 General Plan. Its purpose is to provide the public, the City’s decision-makers, and other agencies with detailed information about San Juan Bautista using current conditions and community input. In this way, the Background Report provides the informational basis on which the goals, policies, and programs of the San Juan Bautista General Plan are in part based. While the General Plan itself represents the official adopted goals and policies of the City, this Background Report provides only information, including the plans and programs of other agencies

Examination of Regional Transit Service Under Contracting: A Case Study in the Greater New Orleans Region, Research Report 10-09

Many local governments and transit agencies in the United States face financial difficulties in providing adequate public transit service in individual systems, and in providing sufficient regional coordination to accommodate transit trips involving at least one transfer between systems. These difficulties can be attributed to the recent economic downturn, continuing withdrawal of the state and federal funds that help support local transit service, a decline in local funding for transit service in inner cities due to ongoing suburbanization, and a distribution of resources that responds to geographic equity without addressing service needs. This study examines two main research questions: (1) the effect of a “delegated management” contract on efficiency and effectiveness within a single transit system, and (2) the effects of a single private firm—contracted separately by more than one agency in the same region—on regional coordination, exploring the case in Greater New Orleans. The current situation in New Orleans exhibits two unique transit service conditions. First, New Orleans Regional Transit Authority (RTA) executed a “delegated management” contract with a multinational private firm, outsourcing more functions (e.g., management, planning, funding) to the contractor than has been typical in the U.S. Second, as the same contractor has also been contracted by another transit agency in an adjacent jurisdiction—Jefferson Transit (JeT), this firm may potentially have economic incentives to improve regional coordination, in order to increase the productivity and effectiveness of its own transit service provision. Although the limited amount of available operation and financial data has prevented us from drawing more definitive conclusions, the findings of this multifaceted study should provide valuable information on a transit service contracting approach new to the U.S.: delegated management. This study also identified a coherent set of indices with which to evaluate the regional coordination of transit service, the present status of coordination among U.S. transit agencies, and barriers that need to be resolved for regional transit coordination to be successful

California Public Policy Bibliography 2001

This is a bibliography of public policy resources for California\u27s economy and population, and various legislative committees

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in F

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii